$all
[1] 2702_3 condition study
Data preparation - raw dataset
Import
Sample size
Data Quality
Manipulation check and bot
Manipulation flag
FALSE TRUE
no_reward 128 0
performance_reward 135 7
FALSE TRUE
both_ori 75 2
EEC_ori 83 19
EEF_ori 73 18Descriptive stats on failed manipulation checks
Reward manipulation check
FALSE TRUE
1 128 0
2 135 7Overall percentages (out of all participants)
FALSE TRUE
1 47.41 0.00
2 50.00 2.59Within each condition (row percentages)
FALSE TRUE
1 100.00 0.00
2 95.07 4.93Total failed manipulation check (Reward condition):7 participants ( 2.59 %)Eco-orientation manipulation check
FALSE TRUE
1 73 18
2 83 19
3 75 2Overall percentages (out of all participants)
FALSE TRUE
1 27.04 6.67
2 30.74 7.04
3 27.78 0.74Within each condition (row percentages)
FALSE TRUE
1 80.22 19.78
2 81.37 18.63
3 97.40 2.60Total failed manipulation check (eco condition):39 participants ( 14.44 %)Correlation between total approvals and passing attention check
`geom_smooth()` using formula = 'y ~ x'
[1] 0.03767484Bot flag
FALSE TRUE
269 1 Attention
Duration
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.750 4.217 5.417 6.241 7.042 25.000 
Outliers defined as 3 std. deviations below or above the meanOutliers on completion time
FALSE TRUE
263 7 On scales
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.8287 1.1507 1.1605 1.4746 2.5300 Flagged outliers based on scales
FALSE TRUE
266 4 Removing bad participants
Exclude participants
| cond.reward_flag | cond.eco_flag | outliers_completion | bot_flag | outliers_scales | n |
|---|---|---|---|---|---|
| TRUE | FALSE | FALSE | FALSE | FALSE | 5 |
| TRUE | TRUE | FALSE | FALSE | FALSE | 2 |
| FALSE | FALSE | FALSE | FALSE | TRUE | 3 |
| FALSE | FALSE | FALSE | TRUE | FALSE | 1 |
| FALSE | FALSE | TRUE | FALSE | FALSE | 7 |
| FALSE | TRUE | FALSE | FALSE | FALSE | 36 |
| FALSE | TRUE | FALSE | FALSE | TRUE | 1 |
total excluded from the dataset$all
[1] 55Filtered dataset
Descriptive on good participants
Descriptive tables
✅ All descriptive tables have been saved to 'descriptive_tables.xlsx' vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 214 43.19 11.62 41 42.61 11.86 19 73 54 0.45 -0.61 0.79 Sex n percent
1 Female 123 57.5
2 Male 90 42.1
3 Prefer not to say 1 0.5 Employment.status n percent
1 Full-Time 160 74.8
2 Part-Time 54 25.2 vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 214 43.19 11.62 41 42.61 11.86 19 73 54 0.45 -0.61 0.79 vars n mean sd median trimmed mad min max range skew kurtosis
X1 1 214 938.69 1034.65 560.5 740.38 576.73 0 5278 5278 2.03 4.44
se
X1 70.73
Conditions
Group statistics
Reward groups
no_reward performance_reward
109 105 Eco orientation groups
both_ori EEC_ori EEF_ori
72 72 70 # A tibble: 6 × 9
Condition_reward_name Condition_eco_name n mean_EEF sd_EEF mean_EEC sd_EEC
<chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
1 no_reward EEC_ori 36 5.42 1.24 4.51 1.46
2 no_reward EEF_ori 37 5.39 0.848 4.09 1.05
3 no_reward both_ori 36 5.09 1.08 4.14 1.05
4 performance_reward EEC_ori 36 5.67 1.05 4.64 1.14
5 performance_reward EEF_ori 33 5.71 1.06 4.47 1.20
6 performance_reward both_ori 36 5.14 1.31 3.97 1.40
# ℹ 2 more variables: mean_IM <dbl>, sd_IM <dbl>
Ease and feedback
Q: Were there anything that you found confusing or difficult to follow/answer?
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 214 4.07 0.82 4.19 4.16 0.91 1.37 5 3.63 -0.81 -0.03 0.06Scales
Descriptive stats on scales
all good
270 214 vars n mean sd median trimmed mad min max range skew kurtosis se
IM1 1 214 5.15 1.17 5 5.22 1.48 1 7 6 -0.75 0.71 0.08
IM2 2 214 5.66 1.08 6 5.78 1.48 1 7 6 -1.32 3.08 0.07
IM3 3 214 5.48 1.17 6 5.60 1.48 1 7 6 -1.31 2.67 0.08
EEF1 4 214 5.28 1.19 5 5.38 1.48 1 7 6 -1.03 1.89 0.08
EEF2 5 214 5.43 1.21 6 5.55 1.48 1 7 6 -0.89 0.87 0.08
EEF3 6 214 5.48 1.15 6 5.58 1.48 1 7 6 -0.93 1.33 0.08
EEC1 7 214 4.25 1.32 4 4.30 1.48 1 7 6 -0.28 -0.50 0.09
EEC2 8 214 4.29 1.44 5 4.37 1.48 1 7 6 -0.42 -0.43 0.10
EEC3 9 214 4.36 1.37 5 4.43 1.48 1 7 6 -0.38 -0.22 0.09
ADT1 10 214 5.39 1.05 5 5.43 1.48 1 7 6 -0.57 0.77 0.07
ADT2 11 214 5.33 1.06 5 5.37 1.48 1 7 6 -0.66 1.00 0.07
ADT3 12 214 5.25 1.20 5 5.32 1.48 1 7 6 -0.60 0.16 0.08
TR1 13 214 3.79 1.50 4 3.81 1.48 1 7 6 -0.09 -0.83 0.10
TR2 14 214 3.76 1.55 4 3.78 1.48 1 7 6 -0.12 -1.13 0.11
TR3 15 214 3.48 1.51 4 3.51 1.48 1 7 6 -0.11 -1.05 0.10Descriptive stats on scales
all good
270 214 Variable vars n mean sd median trimmed mad min max range
IM1 IM1 1 214 5.149533 1.165254 5 5.215116 1.4826 1 7 6
IM2 IM2 2 214 5.663551 1.078548 6 5.784884 1.4826 1 7 6
IM3 IM3 3 214 5.476636 1.165593 6 5.598837 1.4826 1 7 6
EEF1 EEF1 4 214 5.280374 1.189109 5 5.377907 1.4826 1 7 6
EEF2 EEF2 5 214 5.429907 1.211154 6 5.552326 1.4826 1 7 6
EEF3 EEF3 6 214 5.481308 1.149454 6 5.575581 1.4826 1 7 6
EEC1 EEC1 7 214 4.247664 1.317762 4 4.302326 1.4826 1 7 6
EEC2 EEC2 8 214 4.294393 1.438004 5 4.372093 1.4826 1 7 6
EEC3 EEC3 9 214 4.359813 1.369168 5 4.430233 1.4826 1 7 6
ADT1 ADT1 10 214 5.387850 1.045465 5 5.430233 1.4826 1 7 6
ADT2 ADT2 11 214 5.331776 1.060259 5 5.366279 1.4826 1 7 6
ADT3 ADT3 12 214 5.247664 1.198344 5 5.319767 1.4826 1 7 6
TR1 TR1 13 214 3.794393 1.502663 4 3.808140 1.4826 1 7 6
TR2 TR2 14 214 3.761682 1.545622 4 3.779070 1.4826 1 7 6
TR3 TR3 15 214 3.476636 1.509568 4 3.505814 1.4826 1 7 6
skew kurtosis se
IM1 -0.75151662 0.7088089 0.07965512
IM2 -1.31757192 3.0807794 0.07372804
IM3 -1.30711427 2.6734375 0.07967828
EEF1 -1.03398779 1.8891674 0.08128579
EEF2 -0.89419883 0.8716892 0.08279281
EEF3 -0.93296574 1.3260497 0.07857508
EEC1 -0.27581240 -0.4959343 0.09008036
EEC2 -0.41715981 -0.4286639 0.09829994
EEC3 -0.37865475 -0.2174569 0.09359441
ADT1 -0.57402680 0.7699548 0.07146652
ADT2 -0.66312767 1.0036774 0.07247784
ADT3 -0.59607506 0.1612733 0.08191708
TR1 -0.08742161 -0.8302237 0.10271994
TR2 -0.12440585 -1.1321737 0.10565655
TR3 -0.11033638 -1.0525237 0.10319191
Normality tests
Non-normality test across all scales
$IM1
Shapiro-Wilk normality test
data: newX[, i]
W = 0.89828, p-value = 7.037e-11
$IM2
Shapiro-Wilk normality test
data: newX[, i]
W = 0.8369, p-value = 3.068e-14
$IM3
Shapiro-Wilk normality test
data: newX[, i]
W = 0.84179, p-value = 5.232e-14
$EEF1
Shapiro-Wilk normality test
data: newX[, i]
W = 0.86711, p-value = 1.016e-12
$EEF2
Shapiro-Wilk normality test
data: newX[, i]
W = 0.88319, p-value = 8.227e-12
$EEF3
Shapiro-Wilk normality test
data: newX[, i]
W = 0.87878, p-value = 4.549e-12
$EEC1
Shapiro-Wilk normality test
data: newX[, i]
W = 0.93787, p-value = 6.574e-08
$EEC2
Shapiro-Wilk normality test
data: newX[, i]
W = 0.93103, p-value = 1.712e-08
$EEC3
Shapiro-Wilk normality test
data: newX[, i]
W = 0.93714, p-value = 5.665e-08
$ADT1
Shapiro-Wilk normality test
data: newX[, i]
W = 0.89708, p-value = 5.889e-11
$ADT2
Shapiro-Wilk normality test
data: newX[, i]
W = 0.89669, p-value = 5.561e-11
$ADT3
Shapiro-Wilk normality test
data: newX[, i]
W = 0.91222, p-value = 6.201e-10
$TR1
Shapiro-Wilk normality test
data: newX[, i]
W = 0.93235, p-value = 2.209e-08
$TR2
Shapiro-Wilk normality test
data: newX[, i]
W = 0.92099, p-value = 2.744e-09
$TR3
Shapiro-Wilk normality test
data: newX[, i]
W = 0.92493, p-value = 5.528e-09Non-normality test on EEF composite
Shapiro-Wilk normality test
data: data_filtered$EEF_composite
W = 0.91886, p-value = 1.894e-09Non-normality test on EEC composite
Shapiro-Wilk normality test
data: data_filtered$EEC_composite
W = 0.97719, p-value = 0.001521Non-normality test on DV per condition
Reward condition on EEF# A tibble: 2 × 3
Condition_reward_name n shapiro_p
<chr> <int> <dbl>
1 no_reward 109 0.0000692
2 performance_reward 105 0.000000224Reward condition on EEC# A tibble: 2 × 3
Condition_reward_name n shapiro_p
<chr> <int> <dbl>
1 no_reward 109 0.0785
2 performance_reward 105 0.0148Eco condition on EEF# A tibble: 3 × 3
Condition_eco_name n shapiro_p
<chr> <int> <dbl>
1 EEC_ori 72 0.0000168
2 EEF_ori 70 0.00165
3 both_ori 72 0.0000608Eco condition on EEC# A tibble: 3 × 3
Condition_eco_name n shapiro_p
<chr> <int> <dbl>
1 EEC_ori 72 0.0536
2 EEF_ori 70 0.152
3 both_ori 72 0.0106Non-normality test on IM composite
Shapiro-Wilk normality test
data: data_filtered$IM_composite
W = 0.93519, p-value = 3.841e-08Non-normality test on IM per condition
# A tibble: 2 × 3
Condition_reward_name n shapiro_p
<chr> <int> <dbl>
1 no_reward 109 0.0000303
2 performance_reward 105 0.0000587# A tibble: 3 × 3
Condition_eco_name n shapiro_p
<chr> <int> <dbl>
1 EEC_ori 72 0.00132
2 EEF_ori 70 0.0000113
3 both_ori 72 0.0123 Factor analyses
KMO
Kaiser-Meyer-Olkin factor adequacy
Call: KMO(r = efa_data_good)
Overall MSA = 0.86
MSA for each item =
IM1 IM2 IM3 EEF1 EEF2 EEF3 EEC1 EEC2 EEC3 ADT1 ADT2 ADT3 TR1 TR2 TR3
0.92 0.82 0.85 0.88 0.90 0.91 0.91 0.85 0.82 0.90 0.82 0.86 0.90 0.78 0.82 Correlation analysis
Bartlett test
R was not square, finding R from data$chisq
[1] 2708.657
$p.value
[1] 0
$df
[1] 105Correlation matrix
IM1 IM2 IM3 EEF1 EEF2 EEF3 EEC1
IM1 1.0000000 0.71262664 0.62823456 0.5727130 0.6361886 0.5979741 0.4588498
IM2 0.7126266 1.00000000 0.76302737 0.3777322 0.4958078 0.4720588 0.3991398
IM3 0.6282346 0.76302737 1.00000000 0.3502518 0.4394806 0.4587153 0.4607446
EEF1 0.5727130 0.37773216 0.35025185 1.0000000 0.8514946 0.8282150 0.5097636
EEF2 0.6361886 0.49580780 0.43948059 0.8514946 1.0000000 0.8353906 0.5242375
EEF3 0.5979741 0.47205876 0.45871527 0.8282150 0.8353906 1.0000000 0.5098388
EEC1 0.4588498 0.39913977 0.46074456 0.5097636 0.5242375 0.5098388 1.0000000
EEC2 0.5003468 0.35475987 0.35004673 0.5006247 0.5496824 0.5188643 0.6055075
EEC3 0.4840296 0.31126891 0.33918961 0.5231259 0.5489536 0.5457312 0.7049909
ADT1 0.3799424 0.18288808 0.16735796 0.3992853 0.3942005 0.3518112 0.3252536
ADT2 0.2674586 0.09396665 0.08038113 0.2982523 0.2722882 0.2651405 0.2601366
ADT3 0.3801755 0.23186565 0.20079144 0.3826485 0.3597516 0.3288763 0.3177411
TR1 0.3045355 0.22072568 0.21972388 0.3030427 0.2938621 0.3049110 0.3056084
TR2 0.2701257 0.14599969 0.12849649 0.2383268 0.2280353 0.2604160 0.1881631
TR3 0.2608877 0.15086114 0.15311072 0.2965969 0.2751431 0.3136058 0.2448330
EEC2 EEC3 ADT1 ADT2 ADT3 TR1 TR2
IM1 0.5003468 0.4840296 0.3799424 0.26745861 0.3801755 0.3045355 0.2701257
IM2 0.3547599 0.3112689 0.1828881 0.09396665 0.2318656 0.2207257 0.1459997
IM3 0.3500467 0.3391896 0.1673580 0.08038113 0.2007914 0.2197239 0.1284965
EEF1 0.5006247 0.5231259 0.3992853 0.29825231 0.3826485 0.3030427 0.2383268
EEF2 0.5496824 0.5489536 0.3942005 0.27228822 0.3597516 0.2938621 0.2280353
EEF3 0.5188643 0.5457312 0.3518112 0.26514049 0.3288763 0.3049110 0.2604160
EEC1 0.6055075 0.7049909 0.3252536 0.26013655 0.3177411 0.3056084 0.1881631
EEC2 1.0000000 0.8329934 0.3421561 0.27127818 0.3470874 0.3040757 0.2175972
EEC3 0.8329934 1.0000000 0.3841870 0.30223586 0.3431703 0.3441873 0.2714351
ADT1 0.3421561 0.3841870 1.0000000 0.79398707 0.7623848 0.4604202 0.4351735
ADT2 0.2712782 0.3022359 0.7939871 1.00000000 0.7996797 0.4290446 0.4209084
ADT3 0.3470874 0.3431703 0.7623848 0.79967969 1.0000000 0.4403519 0.4020901
TR1 0.3040757 0.3441873 0.4604202 0.42904461 0.4403519 1.0000000 0.8237549
TR2 0.2175972 0.2714351 0.4351735 0.42090838 0.4020901 0.8237549 1.0000000
TR3 0.2248661 0.2959732 0.4475272 0.42286050 0.4638784 0.7947046 0.8698778
TR3
IM1 0.2608877
IM2 0.1508611
IM3 0.1531107
EEF1 0.2965969
EEF2 0.2751431
EEF3 0.3136058
EEC1 0.2448330
EEC2 0.2248661
EEC3 0.2959732
ADT1 0.4475272
ADT2 0.4228605
ADT3 0.4638784
TR1 0.7947046
TR2 0.8698778
TR3 1.0000000
EFA
Parallel analysis suggests that the number of factors = 5 and the number of components = NA Threshold=0.35
Loadings:
MR2 MR5 MR3 MR4 MR1
IM1 NA 0.390 NA 0.636 NA
IM2 NA NA NA 0.897 NA
IM3 NA NA NA 0.783 NA
EEF1 NA 0.849 NA NA NA
EEF2 NA 0.804 NA NA NA
EEF3 NA 0.775 NA NA NA
EEC1 NA NA NA NA 0.589
EEC2 NA NA NA NA 0.740
EEC3 NA NA NA NA 0.933
ADT1 NA NA 0.791 NA NA
ADT2 NA NA 0.880 NA NA
ADT3 NA NA 0.810 NA NA
TR1 0.807 NA NA NA NA
TR2 0.923 NA NA NA NA
TR3 0.868 NA NA NA NA
MR2 MR5 MR3 MR4 MR1
SS loadings NA NA NA NA NA
Proportion Var NA NA NA NA NA
Cumulative Var NA NA NA NA NAFactor Analysis using method = minres
Call: fa(r = cor(efa_data_good, use = "pairwise.complete.obs"), nfactors = 5,
rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
MR2 MR5 MR3 MR4 MR1 h2 u2 com
IM1 0.13 0.39 0.20 0.64 0.26 0.68 0.3179 2.4
IM2 0.06 0.20 0.05 0.90 0.13 0.87 0.1314 1.2
IM3 0.07 0.18 0.02 0.78 0.20 0.69 0.3104 1.3
EEF1 0.12 0.85 0.19 0.18 0.26 0.87 0.1288 1.4
EEF2 0.10 0.80 0.17 0.31 0.29 0.86 0.1375 1.7
EEF3 0.15 0.77 0.13 0.30 0.28 0.81 0.1927 1.7
EEC1 0.11 0.29 0.15 0.30 0.59 0.55 0.4475 2.2
EEC2 0.10 0.28 0.16 0.21 0.74 0.71 0.2942 1.6
EEC3 0.15 0.25 0.15 0.13 0.93 1.00 0.0014 1.3
ADT1 0.25 0.19 0.79 0.08 0.17 0.76 0.2371 1.5
ADT2 0.23 0.09 0.88 0.00 0.11 0.85 0.1502 1.2
ADT3 0.24 0.15 0.81 0.14 0.14 0.78 0.2248 1.4
TR1 0.81 0.10 0.24 0.13 0.17 0.77 0.2346 1.4
TR2 0.92 0.08 0.21 0.06 0.07 0.91 0.0904 1.1
TR3 0.87 0.14 0.24 0.05 0.09 0.84 0.1598 1.2
MR2 MR5 MR3 MR4 MR1
SS loadings 2.55 2.52 2.42 2.24 2.23
Proportion Var 0.17 0.17 0.16 0.15 0.15
Cumulative Var 0.17 0.34 0.50 0.65 0.80
Proportion Explained 0.21 0.21 0.20 0.19 0.19
Cumulative Proportion 0.21 0.42 0.63 0.81 1.00
Mean item complexity = 1.5
Test of the hypothesis that 5 factors are sufficient.
df null model = 105 with the objective function = 13.07
df of the model are 40 and the objective function was 0.23
The root mean square of the residuals (RMSR) is 0.01
The df corrected root mean square of the residuals is 0.02
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR2 MR5 MR3 MR4 MR1
Correlation of (regression) scores with factors 0.97 0.95 0.95 0.94 1.00
Multiple R square of scores with factors 0.93 0.90 0.89 0.89 1.00
Minimum correlation of possible factor scores 0.86 0.79 0.79 0.77 0.99
Loading required namespace: GPArotationThreshold = 0.35
Loadings:
MR1 MR2 MR3 MR5 MR4
IM1 NA NA NA NA 0.574
IM2 NA NA NA NA 0.951
IM3 NA NA NA NA 0.816
EEF1 0.971 NA NA NA NA
EEF2 0.875 NA NA NA NA
EEF3 0.842 NA NA NA NA
EEC1 NA NA NA 0.572 NA
EEC2 NA NA NA 0.774 NA
EEC3 NA NA NA 1.032 NA
ADT1 NA NA 0.820 NA NA
ADT2 NA NA 0.954 NA NA
ADT3 NA NA 0.857 NA NA
TR1 NA 0.825 NA NA NA
TR2 NA 0.980 NA NA NA
TR3 NA 0.903 NA NA NA
MR1 MR2 MR3 MR5 MR4
SS loadings NA NA NA NA NA
Proportion Var NA NA NA NA NA
Cumulative Var NA NA NA NA NA
Factor Correlation Matrix: 1 2 3 4 5
1 1.000 0.310 0.399 0.607 0.530
2 0.310 1.000 0.516 0.312 0.198
3 0.399 0.516 1.000 0.384 0.203
4 0.607 0.312 0.384 1.000 0.418
5 0.530 0.198 0.203 0.418 1.000
1 2 3 4 5
1 1.000 0.311 0.399 0.610 0.531
2 0.311 1.000 0.517 0.314 0.198
3 0.399 0.517 1.000 0.386 0.205
4 0.610 0.314 0.386 1.000 0.419
5 0.531 0.198 0.205 0.419 1.000CFA
lavaan 0.6-19 ended normally after 49 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 40
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 175.241 160.591
Degrees of freedom 80 80
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.091
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 2798.001 1963.914
Degrees of freedom 105 105
P-value 0.000 0.000
Scaling correction factor 1.425
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.965 0.957
Tucker-Lewis Index (TLI) 0.954 0.943
Robust Comparative Fit Index (CFI) 0.967
Robust Tucker-Lewis Index (TLI) 0.956
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3958.712 -3958.712
Scaling correction factor 2.043
for the MLR correction
Loglikelihood unrestricted model (H1) -3871.091 -3871.091
Scaling correction factor 1.408
for the MLR correction
Akaike (AIC) 7997.424 7997.424
Bayesian (BIC) 8132.063 8132.063
Sample-size adjusted Bayesian (SABIC) 8005.313 8005.313
Root Mean Square Error of Approximation:
RMSEA 0.075 0.069
90 Percent confidence interval - lower 0.060 0.054
90 Percent confidence interval - upper 0.090 0.083
P-value H_0: RMSEA <= 0.050 0.004 0.021
P-value H_0: RMSEA >= 0.080 0.287 0.104
Robust RMSEA 0.072
90 Percent confidence interval - lower 0.055
90 Percent confidence interval - upper 0.088
P-value H_0: Robust RMSEA <= 0.050 0.016
P-value H_0: Robust RMSEA >= 0.080 0.205
Standardized Root Mean Square Residual:
SRMR 0.061 0.061
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
EEC =~
EEC1 1.000 1.000 1.000
EEC2 1.287 0.108 11.932 0.000 1.076 1.499
EEC3 1.332 0.093 14.258 0.000 1.149 1.515
EEF =~
EEF1 1.000 1.000 1.000
EEF2 1.045 0.053 19.733 0.000 0.941 1.149
EEF3 0.961 0.056 17.299 0.000 0.852 1.070
ADT =~
ADT1 1.000 1.000 1.000
ADT2 1.037 0.068 15.299 0.000 0.904 1.170
ADT3 1.147 0.076 15.133 0.000 0.998 1.295
IM =~
IM1 1.000 1.000 1.000
IM2 0.999 0.166 6.036 0.000 0.675 1.324
IM3 1.001 0.186 5.387 0.000 0.637 1.365
TR =~
TR1 1.000 1.000 1.000
TR2 1.110 0.055 20.135 0.000 1.002 1.218
TR3 1.060 0.058 18.145 0.000 0.945 1.174
Std.lv Std.all
0.973 0.740
1.252 0.873
1.295 0.948
1.079 0.909
1.127 0.933
1.037 0.904
0.918 0.880
0.952 0.900
1.052 0.880
0.952 0.819
0.952 0.884
0.953 0.819
1.309 0.873
1.452 0.942
1.386 0.921
Covariances:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
EEC ~~
EEF 0.674 0.121 5.566 0.000 0.437 0.911
ADT 0.366 0.085 4.307 0.000 0.200 0.533
IM 0.458 0.139 3.293 0.001 0.186 0.731
TR 0.417 0.103 4.040 0.000 0.215 0.619
EEF ~~
ADT 0.408 0.102 4.021 0.000 0.209 0.607
IM 0.641 0.163 3.933 0.000 0.322 0.960
TR 0.451 0.113 3.997 0.000 0.230 0.672
ADT ~~
IM 0.240 0.116 2.077 0.038 0.014 0.467
TR 0.635 0.106 6.018 0.000 0.428 0.842
IM ~~
TR 0.305 0.137 2.235 0.025 0.038 0.573
Std.lv Std.all
0.642 0.642
0.410 0.410
0.495 0.495
0.327 0.327
0.412 0.412
0.624 0.624
0.320 0.320
0.275 0.275
0.529 0.529
0.245 0.245
Variances:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
.EEC1 0.782 0.093 8.420 0.000 0.600 0.965
.EEC2 0.491 0.104 4.732 0.000 0.287 0.694
.EEC3 0.188 0.061 3.070 0.002 0.068 0.308
.EEF1 0.244 0.045 5.397 0.000 0.155 0.332
.EEF2 0.190 0.047 4.072 0.000 0.098 0.281
.EEF3 0.240 0.063 3.788 0.000 0.116 0.364
.ADT1 0.246 0.057 4.344 0.000 0.135 0.357
.ADT2 0.213 0.053 4.003 0.000 0.109 0.317
.ADT3 0.322 0.088 3.647 0.000 0.149 0.495
.IM1 0.444 0.172 2.589 0.010 0.108 0.781
.IM2 0.252 0.075 3.350 0.001 0.105 0.400
.IM3 0.444 0.220 2.019 0.043 0.013 0.875
.TR1 0.535 0.101 5.302 0.000 0.337 0.733
.TR2 0.269 0.068 3.946 0.000 0.135 0.402
.TR3 0.346 0.088 3.945 0.000 0.174 0.518
EEC 0.946 0.148 6.373 0.000 0.655 1.237
EEF 1.164 0.185 6.288 0.000 0.801 1.527
ADT 0.842 0.124 6.812 0.000 0.600 1.084
IM 0.907 0.217 4.182 0.000 0.482 1.332
TR 1.712 0.186 9.230 0.000 1.349 2.076
Std.lv Std.all
0.782 0.453
0.491 0.238
0.188 0.101
0.244 0.173
0.190 0.130
0.240 0.183
0.246 0.226
0.213 0.190
0.322 0.225
0.444 0.329
0.252 0.218
0.444 0.328
0.535 0.238
0.269 0.113
0.346 0.152
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
1.000 1.000
R-Square:
Estimate
EEC1 0.547
EEC2 0.762
EEC3 0.899
EEF1 0.827
EEF2 0.870
EEF3 0.817
ADT1 0.774
ADT2 0.810
ADT3 0.775
IM1 0.671
IM2 0.782
IM3 0.672
TR1 0.762
TR2 0.887
TR3 0.848Cronbach’s Alpha: EEC EEF ADT IM TR
0.883 0.939 0.914 0.874 0.936 Omega: EEC EEF ADT IM TR
0.902 0.939 0.917 0.882 0.938 Average Variance Extracted (AVE): EEC EEF ADT IM TR
0.742 0.839 0.785 0.705 0.833 $type
[1] "cor.bentler"
$cov
EEC1 EEC2 EEC3 EEF1 EEF2 EEF3 ADT1 ADT2 ADT3 IM1
EEC1 0.000
EEC2 -0.040 0.000
EEC3 0.003 0.005 0.000
EEF1 0.078 -0.009 -0.031 0.000
EEF2 0.081 0.027 -0.019 0.003 0.000
EEF3 0.080 0.012 -0.005 0.006 -0.008 0.000
ADT1 0.058 0.027 0.042 0.069 0.056 0.024 0.000
ADT2 -0.013 -0.051 -0.048 -0.039 -0.074 -0.070 0.002 0.000
ADT3 0.051 0.032 0.001 0.053 0.021 0.001 -0.012 0.008 0.000
IM1 0.159 0.146 0.100 0.108 0.159 0.136 0.182 0.065 0.182 0.000
IM2 0.075 -0.027 -0.104 -0.124 -0.019 -0.027 -0.031 -0.125 0.018 -0.012
IM3 0.161 -0.004 -0.045 -0.115 -0.037 -0.003 -0.031 -0.122 0.003 -0.043
TR1 0.094 0.055 0.073 0.049 0.034 0.053 0.054 0.014 0.034 0.129
TR2 -0.040 -0.052 -0.021 -0.035 -0.053 -0.012 -0.003 -0.027 -0.036 0.081
TR3 0.022 -0.038 0.010 0.029 0.001 0.048 0.019 -0.015 0.035 0.076
IM2 IM3 TR1 TR2 TR3
EEC1
EEC2
EEC3
EEF1
EEF2
EEF3
ADT1
ADT2
ADT3
IM1
IM2 0.000
IM3 0.038 0.000
TR1 0.032 0.044 0.000
TR2 -0.058 -0.061 0.002 0.000
TR3 -0.049 -0.032 -0.009 0.003 0.000
$cov.z
EEC1 EEC2 EEC3 EEF1 EEF2 EEF3 ADT1 ADT2 ADT3 IM1
EEC1 0.000
EEC2 -0.776 0.000
EEC3 0.067 0.068 0.000
EEF1 0.963 -0.095 -0.304 0.000
EEF2 1.141 0.293 -0.196 0.028 0.000
EEF3 1.119 0.142 -0.053 0.057 -0.082 0.000
ADT1 0.882 0.351 0.545 0.821 0.612 0.250 0.000
ADT2 -0.196 -0.659 -0.614 -0.435 -0.811 -0.720 0.027 0.000
ADT3 0.780 0.422 0.010 0.605 0.229 0.007 -0.136 0.089 0.000
IM1 2.501 2.255 1.533 1.266 1.898 1.605 2.128 0.777 2.220 0.000
IM2 1.601 -0.492 -1.946 -1.596 -0.309 -0.451 -0.637 -2.693 0.396 -0.236
IM3 2.402 -0.068 -0.787 -1.434 -0.569 -0.058 -0.562 -2.256 0.049 -0.928
TR1 1.463 0.911 1.217 0.774 0.501 0.778 0.857 0.226 0.543 1.791
TR2 -0.612 -0.791 -0.334 -0.473 -0.699 -0.154 -0.043 -0.419 -0.530 1.297
TR3 0.341 -0.613 0.170 0.429 0.010 0.669 0.288 -0.242 0.556 1.107
IM2 IM3 TR1 TR2 TR3
EEC1
EEC2
EEC3
EEF1
EEF2
EEF3
ADT1
ADT2
ADT3
IM1
IM2 0.000
IM3 0.639 0.000
TR1 0.680 0.829 0.000
TR2 -1.327 -1.298 0.041 0.000
TR3 -1.010 -0.614 -0.252 0.059 0.000
$summary
cov
srmr 0.061
srmr.se 0.035
srmr.exactfit.z 0.000
srmr.exactfit.pvalue 0.500
usrmr 0.000
usrmr.se 0.059
usrmr.ci.lower -0.097
usrmr.ci.upper 0.097
usrmr.closefit.h0.value 0.050
usrmr.closefit.z -0.850
usrmr.closefit.pvalue 0.802SEM
No mediation
No moderation - using individual itemslavaan 0.6-19 ended normally after 54 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 29
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 212.944 200.701
Degrees of freedom 61 61
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.061
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1635.931 1229.293
Degrees of freedom 81 81
P-value 0.000 0.000
Scaling correction factor 1.331
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.902 0.878
Tucker-Lewis Index (TLI) 0.870 0.838
Robust Comparative Fit Index (CFI) 0.903
Robust Tucker-Lewis Index (TLI) 0.871
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2410.470 -2410.470
Scaling correction factor 1.964
for the MLR correction
Loglikelihood unrestricted model (H1) -2303.998 -2303.998
Scaling correction factor 1.352
for the MLR correction
Akaike (AIC) 4878.940 4878.940
Bayesian (BIC) 4976.553 4976.553
Sample-size adjusted Bayesian (SABIC) 4884.660 4884.660
Root Mean Square Error of Approximation:
RMSEA 0.108 0.103
90 Percent confidence interval - lower 0.092 0.088
90 Percent confidence interval - upper 0.124 0.119
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.998 0.994
Robust RMSEA 0.107
90 Percent confidence interval - lower 0.090
90 Percent confidence interval - upper 0.123
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.996
Standardized Root Mean Square Residual:
SRMR 0.194 0.194
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF =~
EEF1 1.000 1.086 0.916
EEF2 1.033 0.052 20.016 0.000 1.122 0.928
EEF3 0.953 0.055 17.192 0.000 1.035 0.903
EEC =~
EEC1 1.000 0.970 0.738
EEC2 1.292 0.108 11.960 0.000 1.253 0.873
EEC3 1.336 0.093 14.406 0.000 1.296 0.949
IM =~
IM1 1.000 0.890 0.766
IM2 1.124 0.160 7.041 0.000 1.001 0.930
IM3 1.071 0.160 6.703 0.000 0.954 0.820
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
reward1_eco1 0.320 0.232 1.382 0.167 0.295 0.107
reward0_eco2 0.028 0.247 0.113 0.910 0.026 0.010
reward1_eco2 0.281 0.221 1.270 0.204 0.258 0.097
reward0_eco3 -0.298 0.226 -1.318 0.187 -0.275 -0.103
reward1_eco3 -0.241 0.257 -0.937 0.349 -0.222 -0.083
EEC ~
reward1_eco1 0.308 0.216 1.427 0.153 0.318 0.115
reward0_eco2 0.284 0.243 1.170 0.242 0.293 0.109
reward1_eco2 0.369 0.222 1.665 0.096 0.380 0.142
reward0_eco3 0.003 0.198 0.013 0.989 0.003 0.001
reward1_eco3 -0.113 0.236 -0.480 0.631 -0.117 -0.044
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF ~~
.EEC 0.639 0.111 5.752 0.000 0.632 0.632
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF1 0.227 0.045 5.040 0.000 0.227 0.161
.EEF2 0.202 0.050 4.038 0.000 0.202 0.138
.EEF3 0.243 0.064 3.805 0.000 0.243 0.185
.EEC1 0.788 0.092 8.525 0.000 0.788 0.456
.EEC2 0.488 0.104 4.717 0.000 0.488 0.237
.EEC3 0.186 0.060 3.119 0.002 0.186 0.100
.IM1 0.559 0.154 3.634 0.000 0.559 0.413
.IM2 0.156 0.050 3.096 0.002 0.156 0.134
.IM3 0.443 0.202 2.190 0.029 0.443 0.327
.EEF 1.126 0.177 6.360 0.000 0.954 0.954
.EEC 0.907 0.139 6.520 0.000 0.964 0.964
IM 0.793 0.185 4.279 0.000 1.000 1.000
R-Square:
Estimate
EEF1 0.839
EEF2 0.862
EEF3 0.815
EEC1 0.544
EEC2 0.763
EEC3 0.900
IM1 0.587
IM2 0.866
IM3 0.673
EEF 0.046
EEC 0.036No moderation - using composite variableslavaan 0.6-19 ended normally after 14 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 13
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 0.000 0.000
Degrees of freedom 0 0
Model Test Baseline Model:
Test statistic 117.595 117.789
Degrees of freedom 11 11
P-value 0.000 0.000
Scaling correction factor 0.998
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.000 1.000
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -617.053 -617.053
Loglikelihood unrestricted model (H1) -617.053 -617.053
Akaike (AIC) 1260.105 1260.105
Bayesian (BIC) 1303.863 1303.863
Sample-size adjusted Bayesian (SABIC) 1262.669 1262.669
Root Mean Square Error of Approximation:
RMSEA 0.000 NA
90 Percent confidence interval - lower 0.000 NA
90 Percent confidence interval - upper 0.000 NA
P-value H_0: RMSEA <= 0.050 NA NA
P-value H_0: RMSEA >= 0.080 NA NA
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.000
P-value H_0: Robust RMSEA <= 0.050 NA
P-value H_0: Robust RMSEA >= 0.080 NA
Standardized Root Mean Square Residual:
SRMR 0.000 0.000
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF_composite ~
reward1_eco1 0.320 0.227 1.406 0.160 0.320 0.104
reward0_eco2 0.029 0.245 0.119 0.905 0.029 0.010
reward1_eco2 0.279 0.221 1.265 0.206 0.279 0.094
reward0_eco3 -0.295 0.224 -1.313 0.189 -0.295 -0.099
reward1_eco3 -0.248 0.255 -0.973 0.331 -0.248 -0.083
EEC_composite ~
reward1_eco1 0.385 0.267 1.443 0.149 0.385 0.112
reward0_eco2 0.419 0.294 1.424 0.155 0.419 0.127
reward1_eco2 0.549 0.253 2.171 0.030 0.549 0.166
reward0_eco3 0.049 0.242 0.201 0.840 0.049 0.015
reward1_eco3 -0.118 0.286 -0.412 0.680 -0.118 -0.036
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF_composite ~~
.EEC_composite 0.804 0.119 6.753 0.000 0.804 0.609
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF_composite 1.190 0.156 7.613 0.000 1.190 0.957
.EEC_composite 1.464 0.134 10.921 0.000 1.464 0.960
R-Square:
Estimate
EEF_composite 0.043
EEC_composite 0.040Only reward
lavaan 0.6-19 ended normally after 34 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 21
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 186.567 162.707
Degrees of freedom 33 33
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.147
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1598.761 986.859
Degrees of freedom 45 45
P-value 0.000 0.000
Scaling correction factor 1.620
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.901 0.862
Tucker-Lewis Index (TLI) 0.865 0.812
Robust Comparative Fit Index (CFI) 0.903
Robust Tucker-Lewis Index (TLI) 0.867
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2415.866 -2415.866
Scaling correction factor 2.330
for the MLR correction
Loglikelihood unrestricted model (H1) -2322.583 -2322.583
Scaling correction factor 1.607
for the MLR correction
Akaike (AIC) 4873.732 4873.732
Bayesian (BIC) 4944.418 4944.418
Sample-size adjusted Bayesian (SABIC) 4877.874 4877.874
Root Mean Square Error of Approximation:
RMSEA 0.147 0.136
90 Percent confidence interval - lower 0.127 0.116
90 Percent confidence interval - upper 0.168 0.155
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 1.000 1.000
Robust RMSEA 0.145
90 Percent confidence interval - lower 0.123
90 Percent confidence interval - upper 0.168
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.262 0.262
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF =~
EEF1 1.000 1.086 0.915
EEF2 1.034 0.052 19.856 0.000 1.123 0.929
EEF3 0.953 0.055 17.218 0.000 1.035 0.903
EEC =~
EEC1 1.000 0.968 0.736
EEC2 1.290 0.108 11.992 0.000 1.249 0.870
EEC3 1.345 0.094 14.378 0.000 1.301 0.953
IM =~
IM1 1.000 0.890 0.766
IM2 1.124 0.160 7.041 0.000 1.001 0.930
IM3 1.071 0.160 6.703 0.000 0.954 0.820
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
Condition_rwrd 0.204 0.152 1.341 0.180 0.188 0.094
EEC ~
Condition_rwrd 0.091 0.138 0.657 0.511 0.094 0.047
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF ~~
.EEC 0.667 0.122 5.465 0.000 0.638 0.638
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF1 0.228 0.045 5.094 0.000 0.228 0.162
.EEF2 0.200 0.050 3.999 0.000 0.200 0.137
.EEF3 0.244 0.064 3.824 0.000 0.244 0.185
.EEC1 0.792 0.093 8.519 0.000 0.792 0.458
.EEC2 0.499 0.104 4.805 0.000 0.499 0.243
.EEC3 0.172 0.059 2.926 0.003 0.172 0.092
.IM1 0.559 0.154 3.634 0.000 0.559 0.413
.IM2 0.156 0.050 3.096 0.002 0.156 0.134
.IM3 0.443 0.202 2.190 0.029 0.443 0.327
.EEF 1.169 0.187 6.234 0.000 0.991 0.991
.EEC 0.935 0.147 6.361 0.000 0.998 0.998
IM 0.793 0.185 4.279 0.000 1.000 1.000
R-Square:
Estimate
EEF1 0.838
EEF2 0.863
EEF3 0.815
EEC1 0.542
EEC2 0.757
EEC3 0.908
IM1 0.587
IM2 0.866
IM3 0.673
EEF 0.009
EEC 0.002Only eco-conditions - as predictors
lavaan 0.6-19 ended normally after 40 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 23
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 196.426 181.437
Degrees of freedom 40 40
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.083
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1615.752 1086.268
Degrees of freedom 54 54
P-value 0.000 0.000
Scaling correction factor 1.487
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.900 0.863
Tucker-Lewis Index (TLI) 0.865 0.815
Robust Comparative Fit Index (CFI) 0.900
Robust Tucker-Lewis Index (TLI) 0.865
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2412.300 -2412.300
Scaling correction factor 2.213
for the MLR correction
Loglikelihood unrestricted model (H1) -2314.087 -2314.087
Scaling correction factor 1.495
for the MLR correction
Akaike (AIC) 4870.600 4870.600
Bayesian (BIC) 4948.018 4948.018
Sample-size adjusted Bayesian (SABIC) 4875.136 4875.136
Root Mean Square Error of Approximation:
RMSEA 0.135 0.129
90 Percent confidence interval - lower 0.117 0.111
90 Percent confidence interval - upper 0.154 0.147
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 1.000 1.000
Robust RMSEA 0.134
90 Percent confidence interval - lower 0.114
90 Percent confidence interval - upper 0.154
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.245 0.245
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF =~
EEF1 1.000 1.087 0.916
EEF2 1.032 0.052 20.012 0.000 1.121 0.928
EEF3 0.953 0.056 17.097 0.000 1.036 0.903
EEC =~
EEC1 1.000 0.970 0.738
EEC2 1.290 0.107 12.000 0.000 1.251 0.872
EEC3 1.339 0.093 14.353 0.000 1.298 0.950
IM =~
IM1 1.000 0.890 0.766
IM2 1.124 0.160 7.041 0.000 1.001 0.930
IM3 1.071 0.160 6.703 0.000 0.954 0.820
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
Condition_both -0.421 0.183 -2.304 0.021 -0.387 -0.183
Condition_EEC 0.003 0.177 0.018 0.986 0.003 0.001
EEC ~
Condition_both -0.201 0.160 -1.258 0.208 -0.207 -0.098
Condition_EEC 0.179 0.171 1.047 0.295 0.185 0.087
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF ~~
.EEC 0.647 0.112 5.757 0.000 0.633 0.633
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF1 0.227 0.045 5.018 0.000 0.227 0.161
.EEF2 0.203 0.050 4.059 0.000 0.203 0.139
.EEF3 0.242 0.064 3.791 0.000 0.242 0.184
.EEC1 0.788 0.093 8.510 0.000 0.788 0.456
.EEC2 0.494 0.102 4.823 0.000 0.494 0.240
.EEC3 0.181 0.059 3.054 0.002 0.181 0.097
.IM1 0.559 0.154 3.634 0.000 0.559 0.413
.IM2 0.156 0.050 3.096 0.002 0.156 0.134
.IM3 0.443 0.202 2.190 0.029 0.443 0.327
.EEF 1.141 0.177 6.452 0.000 0.966 0.966
.EEC 0.916 0.141 6.509 0.000 0.974 0.974
IM 0.793 0.185 4.279 0.000 1.000 1.000 lhs op rhs est se z pvalue ci.lower ci.upper std.lv
10 EEF ~ Condition_both -0.421 0.183 -2.304 0.021 -0.779 -0.063 -0.387
11 EEF ~ Condition_EEC 0.003 0.177 0.018 0.986 -0.344 0.350 0.003
12 EEC ~ Condition_both -0.201 0.160 -1.258 0.208 -0.514 0.112 -0.207
13 EEC ~ Condition_EEC 0.179 0.171 1.047 0.295 -0.156 0.515 0.185
std.all std.nox
10 -0.183 -0.387
11 0.001 0.003
12 -0.098 -0.207
13 0.087 0.185Only EEF
lavaan 0.6-19 ended normally after 29 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 11
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 6.372 6.056
Degrees of freedom 10 10
P-value (Chi-square) 0.783 0.811
Scaling correction factor 1.052
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 586.644 458.910
Degrees of freedom 18 18
P-value 0.000 0.000
Scaling correction factor 1.278
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.011 1.016
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.013
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -727.189 -727.189
Scaling correction factor 1.599
for the MLR correction
Loglikelihood unrestricted model (H1) -724.003 -724.003
Scaling correction factor 1.339
for the MLR correction
Akaike (AIC) 1476.378 1476.378
Bayesian (BIC) 1513.404 1513.404
Sample-size adjusted Bayesian (SABIC) 1478.548 1478.548
Root Mean Square Error of Approximation:
RMSEA 0.000 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.050 0.045
P-value H_0: RMSEA <= 0.050 0.951 0.963
P-value H_0: RMSEA >= 0.080 0.004 0.002
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.048
P-value H_0: Robust RMSEA <= 0.050 0.956
P-value H_0: Robust RMSEA >= 0.080 0.004
Standardized Root Mean Square Residual:
SRMR 0.011 0.011
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF =~
EEF1 1.000 1.090 0.919
EEF2 1.027 0.051 20.078 0.000 1.119 0.926
EEF3 0.949 0.057 16.663 0.000 1.034 0.902
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
reward1_eco1 0.320 0.232 1.378 0.168 0.294 0.106
reward0_eco2 0.026 0.248 0.104 0.917 0.024 0.009
reward1_eco2 0.279 0.222 1.257 0.209 0.256 0.096
reward0_eco3 -0.300 0.227 -1.323 0.186 -0.276 -0.103
reward1_eco3 -0.244 0.258 -0.944 0.345 -0.224 -0.084
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF1 0.220 0.046 4.733 0.000 0.220 0.156
.EEF2 0.207 0.052 3.979 0.000 0.207 0.142
.EEF3 0.245 0.066 3.710 0.000 0.245 0.186
.EEF 1.133 0.177 6.399 0.000 0.954 0.954
R-Square:
Estimate
EEF1 0.844
EEF2 0.858
EEF3 0.814
EEF 0.046Only EEC
lavaan 0.6-19 ended normally after 31 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 11
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 12.579 12.705
Degrees of freedom 10 10
P-value (Chi-square) 0.248 0.241
Scaling correction factor 0.990
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 419.779 376.677
Degrees of freedom 18 18
P-value 0.000 0.000
Scaling correction factor 1.114
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.994 0.992
Tucker-Lewis Index (TLI) 0.988 0.986
Robust Comparative Fit Index (CFI) 0.993
Robust Tucker-Lewis Index (TLI) 0.988
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -909.881 -909.881
Scaling correction factor 1.148
for the MLR correction
Loglikelihood unrestricted model (H1) -903.592 -903.592
Scaling correction factor 1.073
for the MLR correction
Akaike (AIC) 1841.763 1841.763
Bayesian (BIC) 1878.789 1878.789
Sample-size adjusted Bayesian (SABIC) 1843.932 1843.932
Root Mean Square Error of Approximation:
RMSEA 0.035 0.036
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.086 0.087
P-value H_0: RMSEA <= 0.050 0.626 0.617
P-value H_0: RMSEA >= 0.080 0.080 0.084
Robust RMSEA 0.035
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.086
P-value H_0: Robust RMSEA <= 0.050 0.621
P-value H_0: Robust RMSEA >= 0.080 0.081
Standardized Root Mean Square Residual:
SRMR 0.023 0.023
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.947 0.720
EEC2 1.291 0.106 12.136 0.000 1.222 0.852
EEC3 1.410 0.108 13.109 0.000 1.335 0.977
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC ~
reward1_eco1 0.286 0.209 1.372 0.170 0.302 0.109
reward0_eco2 0.246 0.233 1.058 0.290 0.260 0.097
reward1_eco2 0.316 0.220 1.438 0.150 0.334 0.125
reward0_eco3 -0.028 0.191 -0.149 0.882 -0.030 -0.011
reward1_eco3 -0.100 0.230 -0.437 0.662 -0.106 -0.040
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.831 0.093 8.896 0.000 0.831 0.481
.EEC2 0.564 0.123 4.568 0.000 0.564 0.274
.EEC3 0.083 0.076 1.100 0.271 0.083 0.045
.EEC 0.869 0.137 6.328 0.000 0.969 0.969
R-Square:
Estimate
EEC1 0.519
EEC2 0.726
EEC3 0.955
EEC 0.031Mediation
Non-connected DVs
Simple mediation on both dependent variableslavaan 0.6-19 ended normally after 55 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 124.251 121.729
Degrees of freedom 54 54
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.021
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1635.931 1229.293
Degrees of freedom 81 81
P-value 0.000 0.000
Scaling correction factor 1.331
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.955 0.941
Tucker-Lewis Index (TLI) 0.932 0.912
Robust Comparative Fit Index (CFI) 0.955
Robust Tucker-Lewis Index (TLI) 0.932
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2366.123 -2366.123
Scaling correction factor 1.849
for the MLR correction
Loglikelihood unrestricted model (H1) -2303.998 -2303.998
Scaling correction factor 1.352
for the MLR correction
Akaike (AIC) 4804.246 4804.246
Bayesian (BIC) 4925.421 4925.421
Sample-size adjusted Bayesian (SABIC) 4811.346 4811.346
Root Mean Square Error of Approximation:
RMSEA 0.078 0.077
90 Percent confidence interval - lower 0.060 0.059
90 Percent confidence interval - upper 0.096 0.095
P-value H_0: RMSEA <= 0.050 0.007 0.009
P-value H_0: RMSEA >= 0.080 0.444 0.393
Robust RMSEA 0.077
90 Percent confidence interval - lower 0.059
90 Percent confidence interval - upper 0.096
P-value H_0: Robust RMSEA <= 0.050 0.008
P-value H_0: Robust RMSEA >= 0.080 0.423
Standardized Root Mean Square Residual:
SRMR 0.048 0.048
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.974 0.741
EEC2 1.291 0.109 11.847 0.000 1.257 0.876
EEC3 1.324 0.093 14.305 0.000 1.289 0.944
EEF =~
EEF1 1.000 1.078 0.909
EEF2 1.046 0.052 20.023 0.000 1.128 0.933
EEF3 0.962 0.054 17.770 0.000 1.037 0.904
IM =~
IM1 1.000 0.947 0.814
IM2 1.008 0.159 6.345 0.000 0.954 0.887
IM3 1.011 0.178 5.669 0.000 0.957 0.823
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
reward1_eco1 0.321 0.217 1.477 0.140 0.339 0.122
reward0_eco2 0.311 0.219 1.421 0.155 0.328 0.123
reward1_eco2 0.405 0.194 2.086 0.037 0.428 0.160
reward0_eco3 -0.085 0.261 -0.324 0.746 -0.089 -0.033
reward1_eco3 -0.231 0.249 -0.927 0.354 -0.243 -0.091
EEF ~
IM 0.697 0.075 9.275 0.000 0.612 0.612
reward1_eco1 0.097 0.185 0.522 0.602 0.090 0.032
reward0_eco2 -0.184 0.203 -0.909 0.363 -0.171 -0.064
reward1_eco2 0.002 0.195 0.008 0.994 0.001 0.001
reward0_eco3 -0.234 0.178 -1.312 0.189 -0.217 -0.081
reward1_eco3 -0.073 0.205 -0.353 0.724 -0.067 -0.025
EEC ~
IM 0.492 0.075 6.547 0.000 0.478 0.478
reward1_eco1 0.154 0.205 0.748 0.454 0.158 0.057
reward0_eco2 0.136 0.221 0.617 0.537 0.140 0.052
reward1_eco2 0.178 0.214 0.831 0.406 0.182 0.068
reward0_eco3 0.049 0.184 0.267 0.789 0.050 0.019
reward1_eco3 -0.002 0.222 -0.007 0.994 -0.002 -0.001
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.352 0.107 3.281 0.001 0.498 0.498
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.780 0.093 8.413 0.000 0.780 0.451
.EEC2 0.478 0.104 4.602 0.000 0.478 0.232
.EEC3 0.203 0.062 3.296 0.001 0.203 0.109
.EEF1 0.246 0.046 5.369 0.000 0.246 0.174
.EEF2 0.188 0.046 4.097 0.000 0.188 0.129
.EEF3 0.240 0.063 3.805 0.000 0.240 0.183
.IM1 0.455 0.164 2.769 0.006 0.455 0.337
.IM2 0.248 0.067 3.672 0.000 0.248 0.214
.IM3 0.436 0.212 2.054 0.040 0.436 0.323
.EEC 0.710 0.115 6.173 0.000 0.749 0.749
.EEF 0.701 0.165 4.250 0.000 0.603 0.603
.IM 0.840 0.205 4.107 0.000 0.937 0.937
R-Square:
Estimate
EEC1 0.549
EEC2 0.768
EEC3 0.891
EEF1 0.826
EEF2 0.871
EEF3 0.817
IM1 0.663
IM2 0.786
IM3 0.677
EEC 0.251
EEF 0.397
IM 0.063Simple mediation on both dependent variableslavaan 0.6-19 ended normally after 9 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 21
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 0.000 0.000
Degrees of freedom 0 0
Model Test Baseline Model:
Test statistic 221.582 210.615
Degrees of freedom 18 18
P-value 0.000 0.000
Scaling correction factor 1.052
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.000 1.000
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -871.672 -871.672
Loglikelihood unrestricted model (H1) -871.672 -871.672
Akaike (AIC) 1785.345 1785.345
Bayesian (BIC) 1856.030 1856.030
Sample-size adjusted Bayesian (SABIC) 1789.487 1789.487
Root Mean Square Error of Approximation:
RMSEA 0.000 NA
90 Percent confidence interval - lower 0.000 NA
90 Percent confidence interval - upper 0.000 NA
P-value H_0: RMSEA <= 0.050 NA NA
P-value H_0: RMSEA >= 0.080 NA NA
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.000
P-value H_0: Robust RMSEA <= 0.050 NA
P-value H_0: Robust RMSEA >= 0.080 NA
Standardized Root Mean Square Residual:
SRMR 0.000 0.000
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM_composite ~
reward1_eco1 0.301 0.220 1.366 0.172 0.301 0.107
reward0_eco2 0.324 0.221 1.462 0.144 0.324 0.119
reward1_eco2 0.398 0.197 2.018 0.044 0.398 0.147
reward0_eco3 -0.084 0.264 -0.318 0.751 -0.084 -0.031
reward1_eco3 -0.232 0.251 -0.923 0.356 -0.232 -0.086
EEF_composite ~
IM_composite 0.625 0.068 9.145 0.000 0.625 0.568
reward1_eco1 0.132 0.186 0.707 0.480 0.132 0.043
reward0_eco2 -0.173 0.205 -0.843 0.399 -0.173 -0.058
reward1_eco2 0.031 0.191 0.161 0.872 0.031 0.010
reward0_eco3 -0.242 0.180 -1.346 0.178 -0.242 -0.081
reward1_eco3 -0.104 0.205 -0.504 0.614 -0.104 -0.035
EEC_composite ~
IM_composite 0.592 0.072 8.179 0.000 0.592 0.486
reward1_eco1 0.206 0.246 0.839 0.401 0.206 0.060
reward0_eco2 0.227 0.262 0.868 0.386 0.227 0.069
reward1_eco2 0.313 0.239 1.313 0.189 0.313 0.095
reward0_eco3 0.098 0.212 0.464 0.643 0.098 0.030
reward1_eco3 0.020 0.261 0.075 0.940 0.020 0.006
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF_composite ~~
.EEC_composite 0.444 0.096 4.625 0.000 0.444 0.466
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.IM_composite 0.973 0.126 7.710 0.000 0.973 0.946
.EEF_composite 0.810 0.124 6.525 0.000 0.810 0.651
.EEC_composite 1.123 0.116 9.704 0.000 1.123 0.736
R-Square:
Estimate
IM_composite 0.054
EEF_composite 0.349
EEC_composite 0.264Simple mediation on both dependent variableslavaan 0.6-19 ended normally after 9 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 11
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 7.341 7.259
Degrees of freedom 10 10
P-value (Chi-square) 0.693 0.701
Scaling correction factor 1.011
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 221.582 210.615
Degrees of freedom 18 18
P-value 0.000 0.000
Scaling correction factor 1.052
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.024 1.026
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.025
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -875.343 -875.343
Scaling correction factor 1.222
for the MLR correction
Loglikelihood unrestricted model (H1) -871.672 -871.672
Scaling correction factor 1.122
for the MLR correction
Akaike (AIC) 1772.686 1772.686
Bayesian (BIC) 1809.712 1809.712
Sample-size adjusted Bayesian (SABIC) 1774.855 1774.855
Root Mean Square Error of Approximation:
RMSEA 0.000 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.058 0.057
P-value H_0: RMSEA <= 0.050 0.919 0.923
P-value H_0: RMSEA >= 0.080 0.008 0.007
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.057
P-value H_0: Robust RMSEA <= 0.050 0.921
P-value H_0: Robust RMSEA >= 0.080 0.008
Standardized Root Mean Square Residual:
SRMR 0.024 0.024
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM_composite ~
reward1_eco1 0.301 0.220 1.366 0.172 0.301 0.107
reward0_eco2 0.324 0.221 1.462 0.144 0.324 0.119
reward1_eco2 0.398 0.197 2.018 0.044 0.398 0.147
reward0_eco3 -0.084 0.264 -0.318 0.751 -0.084 -0.031
reward1_eco3 -0.232 0.251 -0.923 0.356 -0.232 -0.086
EEF_composite ~
IM_composite 0.638 0.070 9.154 0.000 0.638 0.580
EEC_composite ~
IM_composite 0.616 0.073 8.481 0.000 0.616 0.506
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF_composite ~~
.EEC_composite 0.447 0.096 4.675 0.000 0.447 0.462
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.IM_composite 0.973 0.126 7.710 0.000 0.973 0.946
.EEF_composite 0.825 0.124 6.670 0.000 0.825 0.664
.EEC_composite 1.136 0.115 9.837 0.000 1.136 0.744
R-Square:
Estimate
IM_composite 0.054
EEF_composite 0.336
EEC_composite 0.256Also partial mediation
Non-connected DVslavaan 0.6-19 ended normally after 55 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 124.251 121.729
Degrees of freedom 54 54
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.021
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1635.931 1229.293
Degrees of freedom 81 81
P-value 0.000 0.000
Scaling correction factor 1.331
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.955 0.941
Tucker-Lewis Index (TLI) 0.932 0.912
Robust Comparative Fit Index (CFI) 0.955
Robust Tucker-Lewis Index (TLI) 0.932
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2366.123 -2366.123
Scaling correction factor 1.849
for the MLR correction
Loglikelihood unrestricted model (H1) -2303.998 -2303.998
Scaling correction factor 1.352
for the MLR correction
Akaike (AIC) 4804.246 4804.246
Bayesian (BIC) 4925.421 4925.421
Sample-size adjusted Bayesian (SABIC) 4811.346 4811.346
Root Mean Square Error of Approximation:
RMSEA 0.078 0.077
90 Percent confidence interval - lower 0.060 0.059
90 Percent confidence interval - upper 0.096 0.095
P-value H_0: RMSEA <= 0.050 0.007 0.009
P-value H_0: RMSEA >= 0.080 0.444 0.393
Robust RMSEA 0.077
90 Percent confidence interval - lower 0.059
90 Percent confidence interval - upper 0.096
P-value H_0: Robust RMSEA <= 0.050 0.008
P-value H_0: Robust RMSEA >= 0.080 0.423
Standardized Root Mean Square Residual:
SRMR 0.048 0.048
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.974 0.741
EEC2 1.291 0.109 11.847 0.000 1.257 0.876
EEC3 1.324 0.093 14.305 0.000 1.289 0.944
EEF =~
EEF1 1.000 1.078 0.909
EEF2 1.046 0.052 20.023 0.000 1.128 0.933
EEF3 0.962 0.054 17.770 0.000 1.037 0.904
IM =~
IM1 1.000 0.947 0.814
IM2 1.008 0.159 6.345 0.000 0.954 0.887
IM3 1.011 0.178 5.669 0.000 0.957 0.823
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
reward1_eco1 0.321 0.217 1.477 0.140 0.339 0.122
reward0_eco2 0.311 0.219 1.421 0.155 0.328 0.123
reward1_eco2 0.405 0.194 2.086 0.037 0.428 0.160
reward0_eco3 -0.085 0.261 -0.324 0.746 -0.089 -0.033
reward1_eco3 -0.231 0.249 -0.927 0.354 -0.243 -0.091
EEF ~
IM 0.697 0.075 9.275 0.000 0.612 0.612
reward1_eco1 0.097 0.185 0.522 0.602 0.090 0.032
reward0_eco2 -0.184 0.203 -0.909 0.363 -0.171 -0.064
reward1_eco2 0.002 0.195 0.008 0.994 0.001 0.001
reward0_eco3 -0.234 0.178 -1.312 0.189 -0.217 -0.081
reward1_eco3 -0.073 0.205 -0.353 0.724 -0.067 -0.025
EEC ~
IM 0.492 0.075 6.547 0.000 0.478 0.478
reward1_eco1 0.154 0.205 0.748 0.454 0.158 0.057
reward0_eco2 0.136 0.221 0.617 0.537 0.140 0.052
reward1_eco2 0.178 0.214 0.831 0.406 0.182 0.068
reward0_eco3 0.049 0.184 0.267 0.789 0.050 0.019
reward1_eco3 -0.002 0.222 -0.007 0.994 -0.002 -0.001
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.352 0.107 3.281 0.001 0.498 0.498
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.780 0.093 8.413 0.000 0.780 0.451
.EEC2 0.478 0.104 4.602 0.000 0.478 0.232
.EEC3 0.203 0.062 3.296 0.001 0.203 0.109
.EEF1 0.246 0.046 5.369 0.000 0.246 0.174
.EEF2 0.188 0.046 4.097 0.000 0.188 0.129
.EEF3 0.240 0.063 3.805 0.000 0.240 0.183
.IM1 0.455 0.164 2.769 0.006 0.455 0.337
.IM2 0.248 0.067 3.672 0.000 0.248 0.214
.IM3 0.436 0.212 2.054 0.040 0.436 0.323
.EEC 0.710 0.115 6.173 0.000 0.749 0.749
.EEF 0.701 0.165 4.250 0.000 0.603 0.603
.IM 0.840 0.205 4.107 0.000 0.937 0.937
R-Square:
Estimate
EEC1 0.549
EEC2 0.768
EEC3 0.891
EEF1 0.826
EEF2 0.871
EEF3 0.817
IM1 0.663
IM2 0.786
IM3 0.677
EEC 0.251
EEF 0.397
IM 0.063Only EEF
lavaan 0.6-19 ended normally after 33 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 18
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 72.150 74.571
Degrees of freedom 33 33
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.968
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1079.141 722.264
Degrees of freedom 45 45
P-value 0.000 0.000
Scaling correction factor 1.494
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.962 0.939
Tucker-Lewis Index (TLI) 0.948 0.916
Robust Comparative Fit Index (CFI) 0.960
Robust Tucker-Lewis Index (TLI) 0.946
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1504.987 -1504.987
Scaling correction factor 2.595
for the MLR correction
Loglikelihood unrestricted model (H1) -1468.911 -1468.911
Scaling correction factor 1.542
for the MLR correction
Akaike (AIC) 3045.973 3045.973
Bayesian (BIC) 3106.561 3106.561
Sample-size adjusted Bayesian (SABIC) 3049.523 3049.523
Root Mean Square Error of Approximation:
RMSEA 0.074 0.077
90 Percent confidence interval - lower 0.051 0.053
90 Percent confidence interval - upper 0.098 0.100
P-value H_0: RMSEA <= 0.050 0.044 0.032
P-value H_0: RMSEA >= 0.080 0.369 0.432
Robust RMSEA 0.075
90 Percent confidence interval - lower 0.053
90 Percent confidence interval - upper 0.098
P-value H_0: Robust RMSEA <= 0.050 0.034
P-value H_0: Robust RMSEA >= 0.080 0.393
Standardized Root Mean Square Residual:
SRMR 0.045 0.045
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF =~
EEF1 1.000 1.078 0.909
EEF2 1.046 0.052 20.188 0.000 1.128 0.933
EEF3 0.961 0.055 17.472 0.000 1.037 0.904
IM =~
IM1 1.000 0.939 0.808
IM2 1.024 0.153 6.682 0.000 0.961 0.893
IM3 1.019 0.172 5.935 0.000 0.957 0.823
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
reward1_eco1 0.330 0.214 1.544 0.122 0.352 0.127
reward0_eco2 0.290 0.217 1.334 0.182 0.309 0.115
reward1_eco2 0.404 0.191 2.115 0.034 0.430 0.161
reward0_eco3 -0.105 0.256 -0.410 0.682 -0.112 -0.042
reward1_eco3 -0.235 0.245 -0.957 0.339 -0.250 -0.093
EEF ~
IM 0.709 0.075 9.417 0.000 0.617 0.617
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF1 0.245 0.046 5.289 0.000 0.245 0.174
.EEF2 0.188 0.047 4.009 0.000 0.188 0.129
.EEF3 0.240 0.065 3.680 0.000 0.240 0.183
.IM1 0.470 0.157 2.989 0.003 0.470 0.348
.IM2 0.234 0.060 3.912 0.000 0.234 0.202
.IM3 0.437 0.208 2.098 0.036 0.437 0.323
.EEF 0.719 0.163 4.411 0.000 0.619 0.619
.IM 0.824 0.195 4.218 0.000 0.935 0.935
R-Square:
Estimate
EEF1 0.826
EEF2 0.871
EEF3 0.817
IM1 0.652
IM2 0.798
IM3 0.677
EEF 0.381
IM 0.065Only EEC
lavaan 0.6-19 ended normally after 33 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 18
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 73.346 74.807
Degrees of freedom 33 33
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.980
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 873.803 616.512
Degrees of freedom 45 45
P-value 0.000 0.000
Scaling correction factor 1.417
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.951 0.927
Tucker-Lewis Index (TLI) 0.934 0.900
Robust Comparative Fit Index (CFI) 0.949
Robust Tucker-Lewis Index (TLI) 0.931
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1704.410 -1704.410
Scaling correction factor 2.233
for the MLR correction
Loglikelihood unrestricted model (H1) -1667.737 -1667.737
Scaling correction factor 1.423
for the MLR correction
Akaike (AIC) 3444.820 3444.820
Bayesian (BIC) 3505.408 3505.408
Sample-size adjusted Bayesian (SABIC) 3448.370 3448.370
Root Mean Square Error of Approximation:
RMSEA 0.076 0.077
90 Percent confidence interval - lower 0.052 0.054
90 Percent confidence interval - upper 0.099 0.100
P-value H_0: RMSEA <= 0.050 0.037 0.031
P-value H_0: RMSEA >= 0.080 0.399 0.438
Robust RMSEA 0.076
90 Percent confidence interval - lower 0.053
90 Percent confidence interval - upper 0.099
P-value H_0: Robust RMSEA <= 0.050 0.032
P-value H_0: Robust RMSEA >= 0.080 0.414
Standardized Root Mean Square Residual:
SRMR 0.050 0.050
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.967 0.735
EEC2 1.292 0.109 11.807 0.000 1.249 0.871
EEC3 1.346 0.096 14.085 0.000 1.301 0.953
IM =~
IM1 1.000 0.918 0.790
IM2 1.056 0.149 7.069 0.000 0.969 0.900
IM3 1.056 0.165 6.409 0.000 0.969 0.833
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
reward1_eco1 0.330 0.208 1.584 0.113 0.359 0.130
reward0_eco2 0.310 0.212 1.465 0.143 0.338 0.126
reward1_eco2 0.413 0.187 2.210 0.027 0.450 0.168
reward0_eco3 -0.076 0.247 -0.308 0.758 -0.083 -0.031
reward1_eco3 -0.220 0.240 -0.920 0.358 -0.240 -0.090
EEC ~
IM 0.503 0.079 6.364 0.000 0.477 0.477
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.793 0.092 8.626 0.000 0.793 0.459
.EEC2 0.498 0.114 4.379 0.000 0.498 0.242
.EEC3 0.172 0.068 2.516 0.012 0.172 0.092
.IM1 0.509 0.154 3.300 0.001 0.509 0.377
.IM2 0.219 0.050 4.376 0.000 0.219 0.189
.IM3 0.413 0.190 2.173 0.030 0.413 0.306
.EEC 0.722 0.113 6.407 0.000 0.772 0.772
.IM 0.787 0.188 4.193 0.000 0.934 0.934
R-Square:
Estimate
EEC1 0.541
EEC2 0.758
EEC3 0.908
IM1 0.623
IM2 0.811
IM3 0.694
EEC 0.228
IM 0.066Partial mediation EEF->>EEC
Partial mediation EEF --> EEClavaan 0.6-19 ended normally after 52 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 124.251 121.729
Degrees of freedom 54 54
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.021
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1635.931 1229.293
Degrees of freedom 81 81
P-value 0.000 0.000
Scaling correction factor 1.331
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.955 0.941
Tucker-Lewis Index (TLI) 0.932 0.912
Robust Comparative Fit Index (CFI) 0.955
Robust Tucker-Lewis Index (TLI) 0.932
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2366.123 -2366.123
Scaling correction factor 1.849
for the MLR correction
Loglikelihood unrestricted model (H1) -2303.998 -2303.998
Scaling correction factor 1.352
for the MLR correction
Akaike (AIC) 4804.246 4804.246
Bayesian (BIC) 4925.421 4925.421
Sample-size adjusted Bayesian (SABIC) 4811.346 4811.346
Root Mean Square Error of Approximation:
RMSEA 0.078 0.077
90 Percent confidence interval - lower 0.060 0.059
90 Percent confidence interval - upper 0.096 0.095
P-value H_0: RMSEA <= 0.050 0.007 0.009
P-value H_0: RMSEA >= 0.080 0.444 0.393
Robust RMSEA 0.077
90 Percent confidence interval - lower 0.059
90 Percent confidence interval - upper 0.096
P-value H_0: Robust RMSEA <= 0.050 0.008
P-value H_0: Robust RMSEA >= 0.080 0.423
Standardized Root Mean Square Residual:
SRMR 0.048 0.048
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.974 0.741
EEC2 1.291 0.109 11.847 0.000 1.257 0.876
EEC3 1.324 0.093 14.305 0.000 1.289 0.944
EEF =~
EEF1 1.000 1.078 0.909
EEF2 1.046 0.052 20.023 0.000 1.128 0.933
EEF3 0.962 0.054 17.770 0.000 1.037 0.904
IM =~
IM1 1.000 0.947 0.814
IM2 1.008 0.159 6.345 0.000 0.954 0.887
IM3 1.011 0.178 5.669 0.000 0.957 0.823
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
reward1_eco1 0.321 0.217 1.477 0.140 0.339 0.122
reward0_eco2 0.311 0.219 1.421 0.155 0.328 0.123
reward1_eco2 0.405 0.194 2.086 0.037 0.428 0.160
reward0_eco3 -0.085 0.261 -0.324 0.746 -0.089 -0.033
reward1_eco3 -0.231 0.249 -0.927 0.354 -0.243 -0.091
EEF ~
IM 0.697 0.075 9.275 0.000 0.612 0.612
reward1_eco1 0.097 0.185 0.522 0.602 0.090 0.032
reward0_eco2 -0.184 0.203 -0.909 0.363 -0.171 -0.064
reward1_eco2 0.002 0.195 0.008 0.994 0.001 0.001
reward0_eco3 -0.234 0.178 -1.312 0.189 -0.217 -0.081
reward1_eco3 -0.073 0.205 -0.353 0.724 -0.067 -0.025
EEC ~
IM 0.142 0.093 1.531 0.126 0.138 0.138
reward1_eco1 0.105 0.204 0.515 0.607 0.108 0.039
reward0_eco2 0.229 0.198 1.153 0.249 0.235 0.088
reward1_eco2 0.177 0.194 0.912 0.362 0.181 0.068
reward0_eco3 0.167 0.178 0.937 0.349 0.171 0.064
reward1_eco3 0.035 0.202 0.172 0.864 0.036 0.013
EEF 0.502 0.078 6.433 0.000 0.555 0.555
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.780 0.093 8.413 0.000 0.780 0.451
.EEC2 0.478 0.104 4.602 0.000 0.478 0.232
.EEC3 0.203 0.062 3.296 0.001 0.203 0.109
.EEF1 0.246 0.046 5.369 0.000 0.246 0.174
.EEF2 0.188 0.046 4.097 0.000 0.188 0.129
.EEF3 0.240 0.063 3.805 0.000 0.240 0.183
.IM1 0.455 0.164 2.769 0.006 0.455 0.337
.IM2 0.248 0.067 3.672 0.000 0.248 0.214
.IM3 0.436 0.212 2.054 0.040 0.436 0.323
.EEC 0.534 0.084 6.378 0.000 0.563 0.563
.EEF 0.701 0.165 4.250 0.000 0.603 0.603
.IM 0.840 0.205 4.107 0.000 0.937 0.937
R-Square:
Estimate
EEC1 0.549
EEC2 0.768
EEC3 0.891
EEF1 0.826
EEF2 0.871
EEF3 0.817
IM1 0.663
IM2 0.786
IM3 0.677
EEC 0.437
EEF 0.397
IM 0.063Partial EEC->>EEF
Partial mediation EEC --> EEFlavaan 0.6-19 ended normally after 51 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 124.251 121.729
Degrees of freedom 54 54
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.021
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1635.931 1229.293
Degrees of freedom 81 81
P-value 0.000 0.000
Scaling correction factor 1.331
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.955 0.941
Tucker-Lewis Index (TLI) 0.932 0.912
Robust Comparative Fit Index (CFI) 0.955
Robust Tucker-Lewis Index (TLI) 0.932
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2366.123 -2366.123
Scaling correction factor 1.849
for the MLR correction
Loglikelihood unrestricted model (H1) -2303.998 -2303.998
Scaling correction factor 1.352
for the MLR correction
Akaike (AIC) 4804.246 4804.246
Bayesian (BIC) 4925.421 4925.421
Sample-size adjusted Bayesian (SABIC) 4811.346 4811.346
Root Mean Square Error of Approximation:
RMSEA 0.078 0.077
90 Percent confidence interval - lower 0.060 0.059
90 Percent confidence interval - upper 0.096 0.095
P-value H_0: RMSEA <= 0.050 0.007 0.009
P-value H_0: RMSEA >= 0.080 0.444 0.393
Robust RMSEA 0.077
90 Percent confidence interval - lower 0.059
90 Percent confidence interval - upper 0.096
P-value H_0: Robust RMSEA <= 0.050 0.008
P-value H_0: Robust RMSEA >= 0.080 0.423
Standardized Root Mean Square Residual:
SRMR 0.048 0.048
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.974 0.741
EEC2 1.291 0.109 11.847 0.000 1.257 0.876
EEC3 1.324 0.093 14.305 0.000 1.289 0.944
EEF =~
EEF1 1.000 1.078 0.909
EEF2 1.046 0.052 20.023 0.000 1.128 0.933
EEF3 0.962 0.054 17.770 0.000 1.037 0.904
IM =~
IM1 1.000 0.947 0.814
IM2 1.008 0.159 6.345 0.000 0.954 0.887
IM3 1.011 0.178 5.669 0.000 0.957 0.823
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
reward1_eco1 0.321 0.217 1.477 0.140 0.339 0.122
reward0_eco2 0.311 0.219 1.421 0.155 0.328 0.123
reward1_eco2 0.405 0.194 2.086 0.037 0.428 0.160
reward0_eco3 -0.085 0.261 -0.324 0.746 -0.089 -0.033
reward1_eco3 -0.231 0.249 -0.927 0.354 -0.243 -0.091
EEF ~
IM 0.453 0.084 5.416 0.000 0.398 0.398
reward1_eco1 0.020 0.191 0.107 0.915 0.019 0.007
reward0_eco2 -0.252 0.184 -1.365 0.172 -0.233 -0.087
reward1_eco2 -0.086 0.182 -0.475 0.634 -0.080 -0.030
reward0_eco3 -0.258 0.171 -1.511 0.131 -0.240 -0.090
reward1_eco3 -0.072 0.188 -0.382 0.703 -0.067 -0.025
EEC 0.495 0.119 4.165 0.000 0.447 0.447
EEC ~
IM 0.492 0.075 6.547 0.000 0.478 0.478
reward1_eco1 0.154 0.205 0.748 0.454 0.158 0.057
reward0_eco2 0.136 0.221 0.617 0.537 0.140 0.052
reward1_eco2 0.177 0.214 0.831 0.406 0.182 0.068
reward0_eco3 0.049 0.184 0.267 0.789 0.050 0.019
reward1_eco3 -0.002 0.222 -0.007 0.994 -0.002 -0.001
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.780 0.093 8.413 0.000 0.780 0.451
.EEC2 0.478 0.104 4.602 0.000 0.478 0.232
.EEC3 0.203 0.062 3.296 0.001 0.203 0.109
.EEF1 0.246 0.046 5.369 0.000 0.246 0.174
.EEF2 0.188 0.046 4.097 0.000 0.188 0.129
.EEF3 0.240 0.063 3.805 0.000 0.240 0.183
.IM1 0.455 0.164 2.769 0.006 0.455 0.337
.IM2 0.248 0.067 3.672 0.000 0.248 0.214
.IM3 0.436 0.212 2.054 0.040 0.436 0.323
.EEC 0.710 0.115 6.173 0.000 0.749 0.749
.EEF 0.527 0.094 5.626 0.000 0.453 0.453
.IM 0.840 0.205 4.107 0.000 0.937 0.937
R-Square:
Estimate
EEC1 0.549
EEC2 0.768
EEC3 0.891
EEF1 0.826
EEF2 0.871
EEF3 0.817
IM1 0.663
IM2 0.786
IM3 0.677
EEC 0.251
EEF 0.547
IM 0.063IM
IM as DV and only manipulations as IVslavaan 0.6-19 ended normally after 28 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 11
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 5.678 6.197
Degrees of freedom 10 10
P-value (Chi-square) 0.842 0.798
Scaling correction factor 0.916
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 364.355 194.854
Degrees of freedom 18 18
P-value 0.000 0.000
Scaling correction factor 1.870
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.022 1.039
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.019
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -811.818 -811.818
Scaling correction factor 2.800
for the MLR correction
Loglikelihood unrestricted model (H1) -808.979 -808.979
Scaling correction factor 1.903
for the MLR correction
Akaike (AIC) 1645.637 1645.637
Bayesian (BIC) 1682.663 1682.663
Sample-size adjusted Bayesian (SABIC) 1647.806 1647.806
Root Mean Square Error of Approximation:
RMSEA 0.000 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.043 0.051
P-value H_0: RMSEA <= 0.050 0.968 0.947
P-value H_0: RMSEA >= 0.080 0.002 0.005
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.046
P-value H_0: Robust RMSEA <= 0.050 0.961
P-value H_0: Robust RMSEA >= 0.080 0.002
Standardized Root Mean Square Residual:
SRMR 0.015 0.015
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM =~
IM1 1.000 0.889 0.765
IM2 1.125 0.158 7.111 0.000 1.000 0.930
IM3 1.075 0.161 6.681 0.000 0.956 0.822
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
reward1_eco1 0.320 0.203 1.575 0.115 0.361 0.130
reward0_eco2 0.279 0.207 1.349 0.177 0.313 0.117
reward1_eco2 0.388 0.184 2.104 0.035 0.437 0.163
reward0_eco3 -0.077 0.235 -0.327 0.744 -0.087 -0.032
reward1_eco3 -0.211 0.234 -0.901 0.368 -0.237 -0.089
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.IM1 0.561 0.153 3.676 0.000 0.561 0.415
.IM2 0.157 0.049 3.213 0.001 0.157 0.136
.IM3 0.438 0.203 2.162 0.031 0.438 0.324
.IM 0.740 0.179 4.127 0.000 0.937 0.937
R-Square:
Estimate
IM1 0.585
IM2 0.864
IM3 0.676
IM 0.063IM without independent variableslavaan 0.6-19 ended normally after 33 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 21
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 97.943 92.692
Degrees of freedom 24 24
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.057
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1591.464 907.183
Degrees of freedom 36 36
P-value 0.000 0.000
Scaling correction factor 1.754
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.952 0.921
Tucker-Lewis Index (TLI) 0.929 0.882
Robust Comparative Fit Index (CFI) 0.953
Robust Tucker-Lewis Index (TLI) 0.929
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2375.203 -2375.203
Scaling correction factor 2.460
for the MLR correction
Loglikelihood unrestricted model (H1) -2326.231 -2326.231
Scaling correction factor 1.712
for the MLR correction
Akaike (AIC) 4792.406 4792.406
Bayesian (BIC) 4863.092 4863.092
Sample-size adjusted Bayesian (SABIC) 4796.548 4796.548
Root Mean Square Error of Approximation:
RMSEA 0.120 0.116
90 Percent confidence interval - lower 0.096 0.092
90 Percent confidence interval - upper 0.145 0.140
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.996 0.992
Robust RMSEA 0.119
90 Percent confidence interval - lower 0.094
90 Percent confidence interval - upper 0.145
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.994
Standardized Root Mean Square Residual:
SRMR 0.069 0.069
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.973 0.740
EEC2 1.289 0.108 11.885 0.000 1.254 0.874
EEC3 1.330 0.093 14.310 0.000 1.293 0.947
EEF =~
EEF1 1.000 1.077 0.908
EEF2 1.047 0.052 20.022 0.000 1.128 0.933
EEF3 0.963 0.054 17.668 0.000 1.037 0.904
IM =~
IM1 1.000 0.952 0.819
IM2 1.000 0.161 6.229 0.000 0.952 0.885
IM3 1.002 0.180 5.560 0.000 0.953 0.820
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.706 0.074 9.485 0.000 0.624 0.624
EEC ~
IM 0.507 0.076 6.680 0.000 0.496 0.496
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.350 0.107 3.264 0.001 0.492 0.492
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.782 0.093 8.402 0.000 0.782 0.453
.EEC2 0.486 0.104 4.666 0.000 0.486 0.236
.EEC3 0.193 0.061 3.147 0.002 0.193 0.103
.EEF1 0.247 0.046 5.416 0.000 0.247 0.175
.EEF2 0.188 0.046 4.102 0.000 0.188 0.129
.EEF3 0.239 0.064 3.765 0.000 0.239 0.182
.IM1 0.446 0.165 2.698 0.007 0.446 0.330
.IM2 0.251 0.073 3.428 0.001 0.251 0.217
.IM3 0.444 0.218 2.035 0.042 0.444 0.328
.EEC 0.714 0.116 6.168 0.000 0.754 0.754
.EEF 0.709 0.164 4.318 0.000 0.611 0.611
IM 0.906 0.210 4.312 0.000 1.000 1.000
R-Square:
Estimate
EEC1 0.547
EEC2 0.764
EEC3 0.897
EEF1 0.825
EEF2 0.871
EEF3 0.818
IM1 0.670
IM2 0.783
IM3 0.672
EEC 0.246
EEF 0.389ADT and full mediation by IM
lavaan 0.6-19 ended normally after 33 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 29
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 140.722 124.087
Degrees of freedom 49 49
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.134
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 2129.667 1338.116
Degrees of freedom 66 66
P-value 0.000 0.000
Scaling correction factor 1.592
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.956 0.941
Tucker-Lewis Index (TLI) 0.940 0.920
Robust Comparative Fit Index (CFI) 0.958
Robust Tucker-Lewis Index (TLI) 0.943
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3097.704 -3097.704
Scaling correction factor 2.323
for the MLR correction
Loglikelihood unrestricted model (H1) -3027.343 -3027.343
Scaling correction factor 1.576
for the MLR correction
Akaike (AIC) 6253.408 6253.408
Bayesian (BIC) 6351.021 6351.021
Sample-size adjusted Bayesian (SABIC) 6259.127 6259.127
Root Mean Square Error of Approximation:
RMSEA 0.094 0.085
90 Percent confidence interval - lower 0.076 0.067
90 Percent confidence interval - upper 0.112 0.102
P-value H_0: RMSEA <= 0.050 0.000 0.001
P-value H_0: RMSEA >= 0.080 0.896 0.684
Robust RMSEA 0.090
90 Percent confidence interval - lower 0.071
90 Percent confidence interval - upper 0.110
P-value H_0: Robust RMSEA <= 0.050 0.001
P-value H_0: Robust RMSEA >= 0.080 0.811
Standardized Root Mean Square Residual:
SRMR 0.073 0.073
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.974 0.741
EEC2 1.288 0.108 11.902 0.000 1.255 0.874
EEC3 1.325 0.092 14.341 0.000 1.290 0.945
EEF =~
EEF1 1.000 1.077 0.908
EEF2 1.047 0.052 19.987 0.000 1.128 0.933
EEF3 0.962 0.055 17.641 0.000 1.037 0.904
IM =~
IM1 1.000 0.958 0.824
IM2 0.989 0.173 5.718 0.000 0.947 0.881
IM3 0.990 0.194 5.092 0.000 0.949 0.816
ADT =~
ADT1 1.000 0.914 0.877
ADT2 1.043 0.068 15.283 0.000 0.954 0.902
ADT3 1.152 0.077 14.903 0.000 1.054 0.881
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.458 0.088 5.202 0.000 0.408 0.408
EEC 0.491 0.124 3.973 0.000 0.444 0.444
EEC ~
IM 0.422 0.078 5.438 0.000 0.415 0.415
ADT 0.317 0.089 3.584 0.000 0.298 0.298
IM ~
ADT 0.302 0.148 2.038 0.042 0.288 0.288
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.779 0.094 8.331 0.000 0.779 0.451
.EEC2 0.484 0.102 4.765 0.000 0.484 0.235
.EEC3 0.201 0.062 3.246 0.001 0.201 0.108
.EEF1 0.246 0.045 5.416 0.000 0.246 0.175
.EEF2 0.188 0.046 4.102 0.000 0.188 0.129
.EEF3 0.240 0.064 3.772 0.000 0.240 0.182
.IM1 0.433 0.178 2.437 0.015 0.433 0.320
.IM2 0.260 0.085 3.043 0.002 0.260 0.225
.IM3 0.452 0.231 1.958 0.050 0.452 0.334
.ADT1 0.252 0.057 4.390 0.000 0.252 0.231
.ADT2 0.209 0.053 3.949 0.000 0.209 0.187
.ADT3 0.319 0.089 3.596 0.000 0.319 0.223
.EEC 0.634 0.099 6.396 0.000 0.668 0.668
.EEF 0.529 0.093 5.670 0.000 0.456 0.456
.IM 0.842 0.162 5.186 0.000 0.917 0.917
ADT 0.836 0.123 6.786 0.000 1.000 1.000
R-Square:
Estimate
EEC1 0.549
EEC2 0.765
EEC3 0.892
EEF1 0.825
EEF2 0.871
EEF3 0.818
IM1 0.680
IM2 0.775
IM3 0.666
ADT1 0.769
ADT2 0.813
ADT3 0.777
EEC 0.332
EEF 0.544
IM 0.083Moderation
ADT
Continuous ADT
On reward groups
lavaan 0.6-19 ended normally after 46 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 84
Number of observations per group:
no_reward 109
performance_reward 105
Model Test User Model:
Standard Scaled
Test Statistic 211.222 215.445
Degrees of freedom 96 96
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.980
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
no_reward 94.458 94.458
performance_reward 120.987 120.987
Model Test Baseline Model:
Test statistic 2264.264 1602.257
Degrees of freedom 132 132
P-value 0.000 0.000
Scaling correction factor 1.413
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.946 0.919
Tucker-Lewis Index (TLI) 0.926 0.888
Robust Comparative Fit Index (CFI) 0.944
Robust Tucker-Lewis Index (TLI) 0.923
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3054.575 -3054.575
Scaling correction factor 1.806
for the MLR correction
Loglikelihood unrestricted model (H1) -2948.964 -2948.964
Scaling correction factor 1.366
for the MLR correction
Akaike (AIC) 6277.149 6277.149
Bayesian (BIC) 6559.891 6559.891
Sample-size adjusted Bayesian (SABIC) 6293.716 6293.716
Root Mean Square Error of Approximation:
RMSEA 0.106 0.108
90 Percent confidence interval - lower 0.087 0.088
90 Percent confidence interval - upper 0.125 0.127
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.985 0.990
Robust RMSEA 0.107
90 Percent confidence interval - lower 0.088
90 Percent confidence interval - upper 0.126
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.989
Standardized Root Mean Square Residual:
SRMR 0.077 0.077
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [no_reward]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.891 0.724
EEC2 1.469 0.150 9.790 0.000 1.310 0.891
EEC3 1.362 0.129 10.526 0.000 1.214 0.933
EEF =~
EEF1 1.000 1.004 0.895
EEF2 1.043 0.071 14.594 0.000 1.047 0.908
EEF3 1.018 0.050 20.279 0.000 1.021 0.920
IM =~
IM1 1.000 1.136 0.920
IM2 0.860 0.076 11.389 0.000 0.977 0.938
IM3 0.842 0.104 8.123 0.000 0.956 0.803
ADT =~
ADT1 1.000 0.898 0.863
ADT2 0.973 0.080 12.092 0.000 0.873 0.938
ADT3 1.137 0.095 11.950 0.000 1.021 0.867
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.534 0.085 6.262 0.000 0.604 0.604
ADT 0.161 0.090 1.794 0.073 0.144 0.144
EEC ~
IM 0.365 0.088 4.127 0.000 0.465 0.465
ADT 0.176 0.106 1.663 0.096 0.177 0.177
IM ~
ADT 0.295 0.168 1.759 0.079 0.233 0.233
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.254 0.078 3.244 0.001 0.444 0.444
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.165 0.118 35.341 0.000 4.165 3.385
.EEC2 4.275 0.141 30.351 0.000 4.275 2.907
.EEC3 4.294 0.125 34.445 0.000 4.294 3.299
.EEF1 5.193 0.107 48.365 0.000 5.193 4.633
.EEF2 5.303 0.110 47.999 0.000 5.303 4.597
.EEF3 5.404 0.106 50.837 0.000 5.404 4.869
.IM1 5.156 0.118 43.574 0.000 5.156 4.174
.IM2 5.596 0.100 56.098 0.000 5.596 5.373
.IM3 5.431 0.114 47.591 0.000 5.431 4.558
.ADT1 5.394 0.100 54.121 0.000 5.394 5.184
.ADT2 5.422 0.089 60.768 0.000 5.422 5.820
.ADT3 5.229 0.113 46.345 0.000 5.229 4.439
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.719 0.128 5.608 0.000 0.719 0.475
.EEC2 0.447 0.114 3.927 0.000 0.447 0.207
.EEC3 0.220 0.086 2.565 0.010 0.220 0.130
.EEF1 0.249 0.070 3.584 0.000 0.249 0.198
.EEF2 0.235 0.076 3.103 0.002 0.235 0.176
.EEF3 0.188 0.075 2.498 0.012 0.188 0.153
.IM1 0.236 0.072 3.293 0.001 0.236 0.154
.IM2 0.130 0.044 2.971 0.003 0.130 0.120
.IM3 0.505 0.322 1.567 0.117 0.505 0.356
.ADT1 0.277 0.083 3.354 0.001 0.277 0.256
.ADT2 0.105 0.056 1.872 0.061 0.105 0.121
.ADT3 0.346 0.149 2.326 0.020 0.346 0.249
.EEC 0.568 0.133 4.284 0.000 0.714 0.714
.EEF 0.578 0.133 4.335 0.000 0.574 0.574
.IM 1.221 0.202 6.046 0.000 0.946 0.946
ADT 0.806 0.129 6.247 0.000 1.000 1.000
Group 2 [performance_reward]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 1.045 0.751
EEC2 1.159 0.150 7.730 0.000 1.212 0.868
EEC3 1.305 0.133 9.835 0.000 1.364 0.956
EEF =~
EEF1 1.000 1.154 0.928
EEF2 1.032 0.076 13.578 0.000 1.191 0.954
EEF3 0.906 0.094 9.606 0.000 1.046 0.888
IM =~
IM1 1.000 0.734 0.678
IM2 1.293 0.503 2.569 0.010 0.949 0.858
IM3 1.304 0.549 2.377 0.017 0.957 0.846
ADT =~
ADT1 1.000 0.940 0.899
ADT2 1.097 0.110 9.974 0.000 1.031 0.884
ADT3 1.151 0.113 10.219 0.000 1.082 0.892
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.708 0.126 5.600 0.000 0.450 0.450
ADT 0.498 0.163 3.051 0.002 0.405 0.405
EEC ~
IM 0.496 0.133 3.714 0.000 0.348 0.348
ADT 0.479 0.116 4.133 0.000 0.431 0.431
IM ~
ADT 0.199 0.208 0.955 0.340 0.254 0.254
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.272 0.146 1.862 0.063 0.390 0.390
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.333 0.136 31.909 0.000 4.333 3.114
.EEC2 4.314 0.136 31.668 0.000 4.314 3.090
.EEC3 4.429 0.139 31.808 0.000 4.429 3.104
.EEF1 5.371 0.121 44.242 0.000 5.371 4.318
.EEF2 5.562 0.122 45.624 0.000 5.562 4.452
.EEF3 5.562 0.115 48.357 0.000 5.562 4.719
.IM1 5.143 0.106 48.718 0.000 5.143 4.754
.IM2 5.733 0.108 53.099 0.000 5.733 5.182
.IM3 5.524 0.110 50.069 0.000 5.524 4.886
.ADT1 5.381 0.102 52.741 0.000 5.381 5.147
.ADT2 5.238 0.114 45.986 0.000 5.238 4.488
.ADT3 5.267 0.118 44.485 0.000 5.267 4.341
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.844 0.138 6.109 0.000 0.844 0.436
.EEC2 0.481 0.153 3.142 0.002 0.481 0.247
.EEC3 0.174 0.088 1.970 0.049 0.174 0.086
.EEF1 0.216 0.055 3.898 0.000 0.216 0.139
.EEF2 0.141 0.050 2.832 0.005 0.141 0.090
.EEF3 0.295 0.100 2.946 0.003 0.295 0.212
.IM1 0.632 0.301 2.097 0.036 0.632 0.540
.IM2 0.324 0.115 2.811 0.005 0.324 0.265
.IM3 0.363 0.259 1.399 0.162 0.363 0.284
.ADT1 0.210 0.079 2.655 0.008 0.210 0.192
.ADT2 0.299 0.081 3.690 0.000 0.299 0.219
.ADT3 0.301 0.082 3.669 0.000 0.301 0.205
.EEC 0.674 0.134 5.026 0.000 0.617 0.617
.EEF 0.721 0.244 2.958 0.003 0.541 0.541
.IM 0.503 0.238 2.115 0.034 0.935 0.935
ADT 0.883 0.211 4.180 0.000 1.000 1.000On eco groups
lavaan 0.6-19 ended normally after 47 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 126
Number of observations per group:
EEF_ori 70
both_ori 72
EEC_ori 72
Model Test User Model:
Standard Scaled
Test Statistic 260.132 268.098
Degrees of freedom 144 144
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.970
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
EEF_ori 71.903 71.903
both_ori 99.550 99.550
EEC_ori 96.644 96.644
Model Test Baseline Model:
Test statistic 2331.901 1848.415
Degrees of freedom 198 198
P-value 0.000 0.000
Scaling correction factor 1.262
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.946 0.925
Tucker-Lewis Index (TLI) 0.925 0.897
Robust Comparative Fit Index (CFI) 0.942
Robust Tucker-Lewis Index (TLI) 0.920
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2998.573 -2998.573
Scaling correction factor 1.563
for the MLR correction
Loglikelihood unrestricted model (H1) -2868.507 -2868.507
Scaling correction factor 1.247
for the MLR correction
Akaike (AIC) 6249.147 6249.147
Bayesian (BIC) 6673.260 6673.260
Sample-size adjusted Bayesian (SABIC) 6273.997 6273.997
Root Mean Square Error of Approximation:
RMSEA 0.106 0.110
90 Percent confidence interval - lower 0.085 0.089
90 Percent confidence interval - upper 0.127 0.131
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.979 0.989
Robust RMSEA 0.108
90 Percent confidence interval - lower 0.088
90 Percent confidence interval - upper 0.128
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.988
Standardized Root Mean Square Residual:
SRMR 0.078 0.078
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [EEF_ori]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.864 0.681
EEC2 1.290 0.195 6.622 0.000 1.114 0.838
EEC3 1.319 0.170 7.751 0.000 1.139 0.906
EEF =~
EEF1 1.000 0.888 0.855
EEF2 1.129 0.160 7.061 0.000 1.003 0.887
EEF3 0.903 0.128 7.079 0.000 0.802 0.809
IM =~
IM1 1.000 0.961 0.881
IM2 0.978 0.142 6.901 0.000 0.940 0.915
IM3 0.693 0.260 2.664 0.008 0.665 0.570
ADT =~
ADT1 1.000 0.823 0.793
ADT2 1.220 0.169 7.233 0.000 1.003 0.914
ADT3 1.280 0.182 7.017 0.000 1.053 0.905
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.549 0.148 3.705 0.000 0.594 0.594
EEC 0.179 0.159 1.124 0.261 0.174 0.174
ADT 0.103 0.142 0.721 0.471 0.095 0.095
EEC ~
IM 0.263 0.146 1.803 0.071 0.292 0.292
ADT 0.211 0.155 1.367 0.172 0.201 0.201
IM ~
ADT 0.613 0.262 2.339 0.019 0.525 0.525
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.143 0.152 27.333 0.000 4.143 3.267
.EEC2 4.271 0.159 26.868 0.000 4.271 3.211
.EEC3 4.400 0.150 29.260 0.000 4.400 3.497
.EEF1 5.471 0.124 44.095 0.000 5.471 5.270
.EEF2 5.543 0.135 41.042 0.000 5.543 4.906
.EEF3 5.600 0.118 47.260 0.000 5.600 5.649
.IM1 5.200 0.130 39.906 0.000 5.200 4.770
.IM2 5.729 0.123 46.665 0.000 5.729 5.578
.IM3 5.443 0.139 39.034 0.000 5.443 4.665
.ADT1 5.457 0.124 44.001 0.000 5.457 5.259
.ADT2 5.371 0.131 40.941 0.000 5.371 4.893
.ADT3 5.300 0.139 38.124 0.000 5.300 4.557
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.862 0.170 5.062 0.000 0.862 0.536
.EEC2 0.527 0.201 2.618 0.009 0.527 0.298
.EEC3 0.285 0.145 1.968 0.049 0.285 0.180
.EEF1 0.289 0.105 2.748 0.006 0.289 0.268
.EEF2 0.271 0.111 2.448 0.014 0.271 0.212
.EEF3 0.339 0.119 2.850 0.004 0.339 0.345
.IM1 0.266 0.112 2.372 0.018 0.266 0.223
.IM2 0.171 0.098 1.757 0.079 0.171 0.163
.IM3 0.918 0.518 1.774 0.076 0.918 0.675
.ADT1 0.400 0.123 3.243 0.001 0.400 0.371
.ADT2 0.198 0.100 1.972 0.049 0.198 0.164
.ADT3 0.245 0.092 2.648 0.008 0.245 0.181
.EEC 0.607 0.165 3.680 0.000 0.812 0.812
.EEF 0.358 0.152 2.348 0.019 0.454 0.454
.IM 0.669 0.167 3.995 0.000 0.724 0.724
ADT 0.677 0.137 4.924 0.000 1.000 1.000
Group 2 [both_ori]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 1.035 0.759
EEC2 1.135 0.186 6.119 0.000 1.175 0.868
EEC3 1.240 0.156 7.946 0.000 1.283 0.944
EEF =~
EEF1 1.000 1.140 0.940
EEF2 1.067 0.055 19.496 0.000 1.217 0.967
EEF3 0.991 0.099 9.968 0.000 1.130 0.938
IM =~
IM1 1.000 0.988 0.767
IM2 1.162 0.304 3.822 0.000 1.148 0.901
IM3 1.322 0.330 4.010 0.000 1.306 0.948
ADT =~
ADT1 1.000 0.985 0.956
ADT2 0.894 0.069 13.004 0.000 0.881 0.907
ADT3 0.984 0.085 11.591 0.000 0.970 0.831
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.356 0.114 3.111 0.002 0.308 0.308
EEC 0.448 0.173 2.583 0.010 0.406 0.406
ADT 0.282 0.137 2.058 0.040 0.244 0.244
EEC ~
IM 0.389 0.116 3.358 0.001 0.371 0.371
ADT 0.486 0.121 4.009 0.000 0.463 0.463
IM ~
ADT 0.248 0.222 1.118 0.263 0.247 0.247
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.028 0.161 25.057 0.000 4.028 2.953
.EEC2 4.028 0.160 25.247 0.000 4.028 2.975
.EEC3 4.111 0.160 25.656 0.000 4.111 3.024
.EEF1 5.000 0.143 34.966 0.000 5.000 4.121
.EEF2 5.167 0.148 34.841 0.000 5.167 4.106
.EEF3 5.181 0.142 36.461 0.000 5.181 4.297
.IM1 4.917 0.152 32.383 0.000 4.917 3.816
.IM2 5.375 0.150 35.797 0.000 5.375 4.219
.IM3 5.181 0.162 31.908 0.000 5.181 3.760
.ADT1 5.278 0.121 43.462 0.000 5.278 5.122
.ADT2 5.264 0.115 45.965 0.000 5.264 5.417
.ADT3 5.167 0.137 37.578 0.000 5.167 4.429
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.790 0.164 4.805 0.000 0.790 0.425
.EEC2 0.453 0.186 2.434 0.015 0.453 0.247
.EEC3 0.202 0.102 1.991 0.046 0.202 0.109
.EEF1 0.172 0.052 3.299 0.001 0.172 0.117
.EEF2 0.103 0.047 2.180 0.029 0.103 0.065
.EEF3 0.176 0.076 2.302 0.021 0.176 0.121
.IM1 0.684 0.337 2.028 0.043 0.684 0.412
.IM2 0.306 0.084 3.635 0.000 0.306 0.189
.IM3 0.192 0.102 1.874 0.061 0.192 0.101
.ADT1 0.091 0.052 1.746 0.081 0.091 0.085
.ADT2 0.168 0.069 2.426 0.015 0.168 0.178
.ADT3 0.421 0.212 1.985 0.047 0.421 0.309
.EEC 0.602 0.138 4.366 0.000 0.562 0.562
.EEF 0.536 0.119 4.485 0.000 0.412 0.412
.IM 0.916 0.305 3.003 0.003 0.939 0.939
ADT 0.971 0.269 3.608 0.000 1.000 1.000
Group 3 [EEC_ori]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.953 0.765
EEC2 1.459 0.209 6.965 0.000 1.390 0.896
EEC3 1.474 0.182 8.109 0.000 1.405 0.981
EEF =~
EEF1 1.000 1.130 0.911
EEF2 0.983 0.094 10.403 0.000 1.111 0.936
EEF3 0.953 0.069 13.751 0.000 1.077 0.923
IM =~
IM1 1.000 0.832 0.789
IM2 0.823 0.113 7.296 0.000 0.684 0.846
IM3 0.844 0.146 5.781 0.000 0.702 0.906
ADT =~
ADT1 1.000 0.923 0.878
ADT2 1.062 0.116 9.165 0.000 0.980 0.894
ADT3 1.231 0.120 10.222 0.000 1.137 0.910
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.432 0.160 2.708 0.007 0.318 0.318
EEC 0.637 0.216 2.952 0.003 0.537 0.537
ADT 0.046 0.116 0.398 0.690 0.038 0.038
EEC ~
IM 0.549 0.133 4.125 0.000 0.479 0.479
ADT 0.345 0.112 3.077 0.002 0.334 0.334
IM ~
ADT -0.013 0.126 -0.106 0.916 -0.015 -0.015
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.569 0.147 31.136 0.000 4.569 3.669
.EEC2 4.583 0.183 25.053 0.000 4.583 2.953
.EEC3 4.569 0.169 27.075 0.000 4.569 3.191
.EEF1 5.375 0.146 36.753 0.000 5.375 4.331
.EEF2 5.583 0.140 39.902 0.000 5.583 4.702
.EEF3 5.667 0.137 41.214 0.000 5.667 4.857
.IM1 5.333 0.124 42.933 0.000 5.333 5.060
.IM2 5.889 0.095 61.774 0.000 5.889 7.280
.IM3 5.806 0.091 63.540 0.000 5.806 7.488
.ADT1 5.431 0.124 43.810 0.000 5.431 5.163
.ADT2 5.361 0.129 41.476 0.000 5.361 4.888
.ADT3 5.278 0.147 35.836 0.000 5.278 4.223
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.642 0.133 4.838 0.000 0.642 0.414
.EEC2 0.476 0.124 3.829 0.000 0.476 0.198
.EEC3 0.076 0.071 1.073 0.283 0.076 0.037
.EEF1 0.263 0.063 4.190 0.000 0.263 0.171
.EEF2 0.176 0.062 2.823 0.005 0.176 0.125
.EEF3 0.201 0.120 1.681 0.093 0.201 0.148
.IM1 0.419 0.214 1.960 0.050 0.419 0.377
.IM2 0.186 0.065 2.862 0.004 0.186 0.285
.IM3 0.108 0.056 1.940 0.052 0.108 0.179
.ADT1 0.253 0.090 2.828 0.005 0.253 0.229
.ADT2 0.242 0.094 2.578 0.010 0.242 0.201
.ADT3 0.269 0.103 2.608 0.009 0.269 0.172
.EEC 0.603 0.182 3.322 0.001 0.664 0.664
.EEF 0.554 0.153 3.612 0.000 0.434 0.434
.IM 0.692 0.193 3.593 0.000 1.000 1.000
ADT 0.853 0.178 4.791 0.000 1.000 1.000GG plot
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
ADT as categorical variable
SEM
Grouped by reward
Note: High ADT is coded as above 5lavaan 0.6-19 ended normally after 85 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 66
Number of observations per group:
no_reward 109
performance_reward 105
Model Test User Model:
Standard Scaled
Test Statistic 141.219 154.387
Degrees of freedom 60 60
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.915
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
no_reward 66.456 66.456
performance_reward 87.931 87.931
Model Test Baseline Model:
Test statistic 1709.294 1191.994
Degrees of freedom 90 90
P-value 0.000 0.000
Scaling correction factor 1.434
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.950 0.914
Tucker-Lewis Index (TLI) 0.925 0.872
Robust Comparative Fit Index (CFI) 0.945
Robust Tucker-Lewis Index (TLI) 0.918
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2330.439 -2330.439
Scaling correction factor 1.818
for the MLR correction
Loglikelihood unrestricted model (H1) -2259.830 -2259.830
Scaling correction factor 1.388
for the MLR correction
Akaike (AIC) 4792.878 4792.878
Bayesian (BIC) 5015.033 5015.033
Sample-size adjusted Bayesian (SABIC) 4805.895 4805.895
Root Mean Square Error of Approximation:
RMSEA 0.112 0.121
90 Percent confidence interval - lower 0.089 0.097
90 Percent confidence interval - upper 0.137 0.146
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.986 0.996
Robust RMSEA 0.116
90 Percent confidence interval - lower 0.094
90 Percent confidence interval - upper 0.139
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.995
Standardized Root Mean Square Residual:
SRMR 0.072 0.072
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [no_reward]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.891 0.724
EEC2 1.471 0.150 9.787 0.000 1.311 0.891
EEC3 1.362 0.129 10.541 0.000 1.214 0.933
EEF =~
EEF1 1.000 1.003 0.895
EEF2 1.044 0.071 14.641 0.000 1.047 0.908
EEF3 1.018 0.050 20.224 0.000 1.021 0.920
IM =~
IM1 1.000 1.136 0.920
IM2 0.860 0.076 11.286 0.000 0.977 0.938
IM3 0.842 0.104 8.079 0.000 0.956 0.802
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.352 0.081 4.356 0.000 0.398 0.398
EEC 0.430 0.119 3.603 0.000 0.382 0.382
ADT_high 0.311 0.153 2.041 0.041 0.310 0.155
EEC ~
IM 0.356 0.088 4.071 0.000 0.455 0.455
ADT_high 0.307 0.174 1.758 0.079 0.344 0.172
IM ~
ADT_high 0.680 0.231 2.949 0.003 0.599 0.299
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 3.334 0.283 11.780 0.000 3.334 2.710
.EEC2 3.053 0.402 7.590 0.000 3.053 2.076
.EEC3 3.161 0.368 8.588 0.000 3.161 2.429
.EEF1 4.002 0.314 12.751 0.000 4.002 3.570
.EEF2 4.060 0.317 12.816 0.000 4.060 3.520
.EEF3 4.191 0.313 13.402 0.000 4.191 3.777
.IM1 4.126 0.395 10.447 0.000 4.126 3.340
.IM2 4.711 0.337 13.963 0.000 4.711 4.523
.IM3 4.565 0.359 12.714 0.000 4.565 3.831
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.720 0.129 5.596 0.000 0.720 0.476
.EEC2 0.445 0.112 3.978 0.000 0.445 0.206
.EEC3 0.221 0.087 2.546 0.011 0.221 0.130
.EEF1 0.250 0.070 3.586 0.000 0.250 0.199
.EEF2 0.234 0.075 3.105 0.002 0.234 0.176
.EEF3 0.189 0.076 2.488 0.013 0.189 0.153
.IM1 0.236 0.072 3.282 0.001 0.236 0.154
.IM2 0.130 0.044 2.965 0.003 0.130 0.120
.IM3 0.505 0.323 1.565 0.118 0.505 0.356
.EEC 0.569 0.134 4.233 0.000 0.717 0.717
.EEF 0.447 0.108 4.153 0.000 0.444 0.444
.IM 1.175 0.202 5.806 0.000 0.910 0.910
Group 2 [performance_reward]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 1.045 0.751
EEC2 1.162 0.150 7.733 0.000 1.215 0.870
EEC3 1.302 0.132 9.840 0.000 1.361 0.954
EEF =~
EEF1 1.000 1.153 0.926
EEF2 1.034 0.075 13.701 0.000 1.192 0.954
EEF3 0.909 0.093 9.780 0.000 1.047 0.889
IM =~
IM1 1.000 0.737 0.681
IM2 1.289 0.503 2.561 0.010 0.949 0.858
IM3 1.295 0.536 2.415 0.016 0.954 0.844
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.518 0.165 3.137 0.002 0.331 0.331
EEC 0.520 0.211 2.465 0.014 0.472 0.472
ADT_high 0.111 0.188 0.592 0.554 0.096 0.048
EEC ~
IM 0.589 0.133 4.416 0.000 0.415 0.415
ADT_high 0.720 0.197 3.657 0.000 0.689 0.344
IM ~
ADT_high 0.192 0.230 0.837 0.402 0.261 0.130
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 3.071 0.329 9.337 0.000 3.071 2.207
.EEC2 2.848 0.406 7.009 0.000 2.848 2.040
.EEC3 2.786 0.412 6.763 0.000 2.786 1.953
.EEF1 4.396 0.417 10.540 0.000 4.396 3.533
.EEF2 4.553 0.404 11.259 0.000 4.553 3.645
.EEF3 4.675 0.379 12.323 0.000 4.675 3.967
.IM1 4.852 0.383 12.681 0.000 4.852 4.485
.IM2 5.358 0.364 14.714 0.000 5.358 4.843
.IM3 5.147 0.349 14.734 0.000 5.147 4.553
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.844 0.138 6.127 0.000 0.844 0.436
.EEC2 0.472 0.152 3.113 0.002 0.472 0.242
.EEC3 0.184 0.089 2.071 0.038 0.184 0.090
.EEF1 0.219 0.054 4.036 0.000 0.219 0.142
.EEF2 0.140 0.051 2.726 0.006 0.140 0.090
.EEF3 0.292 0.101 2.899 0.004 0.292 0.210
.IM1 0.627 0.299 2.099 0.036 0.627 0.536
.IM2 0.323 0.122 2.657 0.008 0.323 0.264
.IM3 0.368 0.257 1.432 0.152 0.368 0.288
.EEC 0.734 0.164 4.469 0.000 0.672 0.672
.EEF 0.663 0.172 3.853 0.000 0.499 0.499
.IM 0.533 0.285 1.870 0.061 0.983 0.983Grouped by eco condition
lavaan 0.6-19 ended normally after 83 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 66
Number of observations per group:
EEF orientation 70
EEC orientation 72
Model Test User Model:
Standard Scaled
Test Statistic 106.729 104.107
Degrees of freedom 60 60
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.025
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
EEF orientation 42.202 42.202
EEC orientation 61.905 61.905
Model Test Baseline Model:
Test statistic 1052.013 807.509
Degrees of freedom 90 90
P-value 0.000 0.000
Scaling correction factor 1.303
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.951 0.939
Tucker-Lewis Index (TLI) 0.927 0.908
Robust Comparative Fit Index (CFI) 0.952
Robust Tucker-Lewis Index (TLI) 0.927
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1505.216 -1505.216
Scaling correction factor 1.520
for the MLR correction
Loglikelihood unrestricted model (H1) -1451.851 -1451.851
Scaling correction factor 1.284
for the MLR correction
Akaike (AIC) 3142.432 3142.432
Bayesian (BIC) 3337.516 3337.516
Sample-size adjusted Bayesian (SABIC) 3128.688 3128.688
Root Mean Square Error of Approximation:
RMSEA 0.105 0.102
90 Percent confidence interval - lower 0.071 0.068
90 Percent confidence interval - upper 0.137 0.134
P-value H_0: RMSEA <= 0.050 0.006 0.009
P-value H_0: RMSEA >= 0.080 0.896 0.868
Robust RMSEA 0.103
90 Percent confidence interval - lower 0.069
90 Percent confidence interval - upper 0.136
P-value H_0: Robust RMSEA <= 0.050 0.009
P-value H_0: Robust RMSEA >= 0.080 0.875
Standardized Root Mean Square Residual:
SRMR 0.067 0.067
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [EEF orientation]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.861 0.679
EEC2 1.302 0.198 6.570 0.000 1.121 0.843
EEC3 1.316 0.168 7.834 0.000 1.134 0.901
EEF =~
EEF1 1.000 0.886 0.854
EEF2 1.132 0.157 7.221 0.000 1.004 0.888
EEF3 0.905 0.127 7.102 0.000 0.803 0.810
IM =~
IM1 1.000 0.936 0.859
IM2 1.029 0.132 7.770 0.000 0.963 0.938
IM3 0.724 0.246 2.943 0.003 0.678 0.581
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.553 0.143 3.861 0.000 0.584 0.584
EEC 0.191 0.159 1.200 0.230 0.185 0.185
ADT_high 0.247 0.197 1.256 0.209 0.279 0.137
EEC ~
IM 0.332 0.136 2.442 0.015 0.361 0.361
ADT_high 0.172 0.216 0.795 0.427 0.200 0.098
IM ~
ADT_high 0.680 0.290 2.349 0.019 0.727 0.356
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 3.506 0.320 10.948 0.000 3.506 2.765
.EEC2 3.443 0.451 7.638 0.000 3.443 2.588
.EEC3 3.562 0.433 8.229 0.000 3.562 2.831
.EEF1 4.352 0.404 10.765 0.000 4.352 4.192
.EEF2 4.276 0.426 10.039 0.000 4.276 3.784
.EEF3 4.587 0.364 12.610 0.000 4.587 4.627
.IM1 4.112 0.495 8.302 0.000 4.112 3.772
.IM2 4.609 0.476 9.690 0.000 4.609 4.487
.IM3 4.655 0.397 11.729 0.000 4.655 3.990
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.866 0.171 5.068 0.000 0.866 0.539
.EEC2 0.511 0.200 2.559 0.011 0.511 0.289
.EEC3 0.297 0.148 2.014 0.044 0.297 0.188
.EEF1 0.292 0.107 2.732 0.006 0.292 0.271
.EEF2 0.270 0.104 2.596 0.009 0.270 0.211
.EEF3 0.339 0.120 2.831 0.005 0.339 0.345
.IM1 0.313 0.101 3.105 0.002 0.313 0.263
.IM2 0.127 0.087 1.457 0.145 0.127 0.120
.IM3 0.902 0.506 1.781 0.075 0.902 0.663
.EEC 0.620 0.172 3.592 0.000 0.835 0.835
.EEF 0.355 0.153 2.314 0.021 0.452 0.452
.IM 0.765 0.240 3.189 0.001 0.873 0.873
Group 2 [EEC orientation]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.954 0.766
EEC2 1.459 0.210 6.962 0.000 1.392 0.897
EEC3 1.471 0.182 8.102 0.000 1.403 0.980
EEF =~
EEF1 1.000 1.131 0.911
EEF2 0.982 0.094 10.471 0.000 1.110 0.935
EEF3 0.953 0.070 13.695 0.000 1.077 0.923
IM =~
IM1 1.000 0.831 0.788
IM2 0.822 0.114 7.235 0.000 0.683 0.844
IM3 0.847 0.150 5.652 0.000 0.704 0.908
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.418 0.156 2.680 0.007 0.307 0.307
EEC 0.665 0.214 3.109 0.002 0.561 0.561
ADT_high -0.032 0.215 -0.150 0.881 -0.029 -0.014
EEC ~
IM 0.504 0.132 3.825 0.000 0.439 0.439
ADT_high 0.595 0.196 3.043 0.002 0.624 0.312
IM ~
ADT_high 0.196 0.214 0.916 0.360 0.236 0.118
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 3.529 0.330 10.702 0.000 3.529 2.834
.EEC2 3.064 0.515 5.953 0.000 3.064 1.974
.EEC3 3.038 0.514 5.913 0.000 3.038 2.122
.EEF1 4.608 0.445 10.349 0.000 4.608 3.714
.EEF2 4.831 0.428 11.287 0.000 4.831 4.069
.EEF3 4.936 0.426 11.599 0.000 4.936 4.231
.IM1 5.039 0.345 14.585 0.000 5.039 4.780
.IM2 5.647 0.288 19.587 0.000 5.647 6.981
.IM3 5.556 0.276 20.166 0.000 5.556 7.167
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.641 0.131 4.872 0.000 0.641 0.413
.EEC2 0.472 0.123 3.836 0.000 0.472 0.196
.EEC3 0.082 0.070 1.175 0.240 0.082 0.040
.EEF1 0.262 0.062 4.243 0.000 0.262 0.170
.EEF2 0.177 0.063 2.788 0.005 0.177 0.126
.EEF3 0.201 0.120 1.674 0.094 0.201 0.147
.IM1 0.421 0.217 1.939 0.052 0.421 0.378
.IM2 0.188 0.066 2.831 0.005 0.188 0.287
.IM3 0.106 0.057 1.865 0.062 0.106 0.176
.EEC 0.617 0.191 3.233 0.001 0.678 0.678
.EEF 0.555 0.154 3.613 0.000 0.434 0.434
.IM 0.681 0.187 3.632 0.000 0.986 0.986GGplot
Reward
Eco-condition
EEC
IM
TR
Continuous TR
On reward groups
lavaan 0.6-19 ended normally after 51 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 84
Number of observations per group:
Control 109
Performance-based reward 105
Model Test User Model:
Standard Scaled
Test Statistic 203.279 216.274
Degrees of freedom 96 96
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.940
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Control 100.170 100.170
Performance-based reward 116.104 116.104
Model Test Baseline Model:
Test statistic 2335.169 1715.730
Degrees of freedom 132 132
P-value 0.000 0.000
Scaling correction factor 1.361
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.951 0.924
Tucker-Lewis Index (TLI) 0.933 0.896
Robust Comparative Fit Index (CFI) 0.948
Robust Tucker-Lewis Index (TLI) 0.928
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3225.606 -3225.606
Scaling correction factor 1.717
for the MLR correction
Loglikelihood unrestricted model (H1) -3123.966 -3123.966
Scaling correction factor 1.303
for the MLR correction
Akaike (AIC) 6619.212 6619.212
Bayesian (BIC) 6901.954 6901.954
Sample-size adjusted Bayesian (SABIC) 6635.779 6635.779
Root Mean Square Error of Approximation:
RMSEA 0.102 0.108
90 Percent confidence interval - lower 0.083 0.088
90 Percent confidence interval - upper 0.122 0.128
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.968 0.989
Robust RMSEA 0.105
90 Percent confidence interval - lower 0.086
90 Percent confidence interval - upper 0.124
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.985
Standardized Root Mean Square Residual:
SRMR 0.073 0.073
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Control]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.892 0.725
EEC2 1.467 0.150 9.782 0.000 1.309 0.890
EEC3 1.362 0.129 10.522 0.000 1.215 0.933
EEF =~
EEF1 1.000 1.003 0.895
EEF2 1.042 0.071 14.640 0.000 1.046 0.907
EEF3 1.019 0.050 20.517 0.000 1.022 0.921
IM =~
IM1 1.000 1.136 0.919
IM2 0.860 0.075 11.490 0.000 0.977 0.938
IM3 0.842 0.103 8.166 0.000 0.957 0.803
TR =~
TR1 1.000 1.239 0.876
TR2 1.149 0.080 14.339 0.000 1.424 0.939
TR3 1.067 0.084 12.645 0.000 1.322 0.888
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.538 0.082 6.567 0.000 0.609 0.609
TR 0.095 0.071 1.340 0.180 0.118 0.118
EEC ~
IM 0.378 0.088 4.314 0.000 0.481 0.481
TR 0.074 0.078 0.949 0.343 0.102 0.102
IM ~
TR 0.218 0.112 1.944 0.052 0.237 0.237
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.266 0.078 3.387 0.001 0.455 0.455
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.165 0.118 35.341 0.000 4.165 3.385
.EEC2 4.275 0.141 30.351 0.000 4.275 2.907
.EEC3 4.294 0.125 34.445 0.000 4.294 3.299
.EEF1 5.193 0.107 48.365 0.000 5.193 4.633
.EEF2 5.303 0.110 47.999 0.000 5.303 4.597
.EEF3 5.404 0.106 50.837 0.000 5.404 4.869
.IM1 5.156 0.118 43.574 0.000 5.156 4.174
.IM2 5.596 0.100 56.098 0.000 5.596 5.373
.IM3 5.431 0.114 47.591 0.000 5.431 4.558
.TR1 3.826 0.135 28.263 0.000 3.826 2.707
.TR2 3.743 0.145 25.763 0.000 3.743 2.468
.TR3 3.477 0.142 24.404 0.000 3.477 2.337
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.719 0.128 5.625 0.000 0.719 0.475
.EEC2 0.450 0.113 3.979 0.000 0.450 0.208
.EEC3 0.218 0.084 2.589 0.010 0.218 0.129
.EEF1 0.250 0.070 3.576 0.000 0.250 0.199
.EEF2 0.236 0.076 3.096 0.002 0.236 0.178
.EEF3 0.186 0.075 2.493 0.013 0.186 0.151
.IM1 0.236 0.070 3.354 0.001 0.236 0.155
.IM2 0.130 0.042 3.077 0.002 0.130 0.120
.IM3 0.504 0.321 1.570 0.116 0.504 0.355
.TR1 0.463 0.130 3.574 0.000 0.463 0.232
.TR2 0.274 0.111 2.469 0.014 0.274 0.119
.TR3 0.466 0.159 2.924 0.003 0.466 0.211
.EEC 0.584 0.135 4.332 0.000 0.735 0.735
.EEF 0.585 0.128 4.579 0.000 0.581 0.581
.IM 1.217 0.214 5.676 0.000 0.944 0.944
TR 1.534 0.237 6.477 0.000 1.000 1.000
R-Square:
Estimate
EEC1 0.525
EEC2 0.792
EEC3 0.871
EEF1 0.801
EEF2 0.822
EEF3 0.849
IM1 0.845
IM2 0.880
IM3 0.645
TR1 0.768
TR2 0.881
TR3 0.789
EEC 0.265
EEF 0.419
IM 0.056
Group 2 [Performance-based reward]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 1.038 0.746
EEC2 1.162 0.149 7.793 0.000 1.206 0.864
EEC3 1.324 0.138 9.623 0.000 1.374 0.963
EEF =~
EEF1 1.000 1.152 0.926
EEF2 1.034 0.076 13.661 0.000 1.192 0.954
EEF3 0.909 0.093 9.773 0.000 1.048 0.889
IM =~
IM1 1.000 0.737 0.681
IM2 1.287 0.509 2.530 0.011 0.949 0.857
IM3 1.294 0.548 2.364 0.018 0.954 0.844
TR =~
TR1 1.000 1.368 0.864
TR2 1.090 0.080 13.580 0.000 1.491 0.951
TR3 1.059 0.083 12.715 0.000 1.448 0.949
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.783 0.123 6.382 0.000 0.501 0.501
TR 0.220 0.116 1.904 0.057 0.261 0.261
EEC ~
IM 0.534 0.152 3.504 0.000 0.379 0.379
TR 0.272 0.086 3.150 0.002 0.359 0.359
IM ~
TR 0.112 0.108 1.035 0.301 0.207 0.207
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.352 0.169 2.083 0.037 0.454 0.454
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.333 0.136 31.909 0.000 4.333 3.114
.EEC2 4.314 0.136 31.668 0.000 4.314 3.090
.EEC3 4.429 0.139 31.808 0.000 4.429 3.104
.EEF1 5.371 0.121 44.242 0.000 5.371 4.318
.EEF2 5.562 0.122 45.624 0.000 5.562 4.452
.EEF3 5.562 0.115 48.357 0.000 5.562 4.719
.IM1 5.143 0.106 48.718 0.000 5.143 4.754
.IM2 5.733 0.108 53.099 0.000 5.733 5.182
.IM3 5.524 0.110 50.069 0.000 5.524 4.886
.TR1 3.762 0.154 24.355 0.000 3.762 2.377
.TR2 3.781 0.153 24.718 0.000 3.781 2.412
.TR3 3.476 0.149 23.358 0.000 3.476 2.279
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.859 0.138 6.217 0.000 0.859 0.444
.EEC2 0.495 0.157 3.161 0.002 0.495 0.254
.EEC3 0.147 0.093 1.584 0.113 0.147 0.072
.EEF1 0.220 0.054 4.045 0.000 0.220 0.142
.EEF2 0.140 0.052 2.702 0.007 0.140 0.090
.EEF3 0.291 0.100 2.911 0.004 0.291 0.209
.IM1 0.627 0.305 2.058 0.040 0.627 0.536
.IM2 0.324 0.122 2.660 0.008 0.324 0.265
.IM3 0.368 0.262 1.406 0.160 0.368 0.288
.TR1 0.634 0.164 3.864 0.000 0.634 0.253
.TR2 0.233 0.078 2.999 0.003 0.233 0.095
.TR3 0.229 0.084 2.734 0.006 0.229 0.099
.EEC 0.723 0.155 4.675 0.000 0.671 0.671
.EEF 0.832 0.296 2.814 0.005 0.626 0.626
.IM 0.520 0.269 1.933 0.053 0.957 0.957
TR 1.871 0.291 6.421 0.000 1.000 1.000
R-Square:
Estimate
EEC1 0.556
EEC2 0.746
EEC3 0.928
EEF1 0.858
EEF2 0.910
EEF3 0.791
IM1 0.464
IM2 0.735
IM3 0.712
TR1 0.747
TR2 0.905
TR3 0.901
EEC 0.329
EEF 0.374
IM 0.043On eco groups
lavaan 0.6-19 ended normally after 47 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 126
Number of observations per group:
EEF orientation 70
Combination of EEF and EEC 72
EEC orientation 72
Model Test User Model:
Standard Scaled
Test Statistic 260.132 268.098
Degrees of freedom 144 144
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.970
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
EEF orientation 71.903 71.903
Combination of EEF and EEC 99.550 99.550
EEC orientation 96.644 96.644
Model Test Baseline Model:
Test statistic 2331.901 1848.415
Degrees of freedom 198 198
P-value 0.000 0.000
Scaling correction factor 1.262
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.946 0.925
Tucker-Lewis Index (TLI) 0.925 0.897
Robust Comparative Fit Index (CFI) 0.942
Robust Tucker-Lewis Index (TLI) 0.920
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2998.573 -2998.573
Scaling correction factor 1.563
for the MLR correction
Loglikelihood unrestricted model (H1) -2868.507 -2868.507
Scaling correction factor 1.247
for the MLR correction
Akaike (AIC) 6249.147 6249.147
Bayesian (BIC) 6673.260 6673.260
Sample-size adjusted Bayesian (SABIC) 6273.997 6273.997
Root Mean Square Error of Approximation:
RMSEA 0.106 0.110
90 Percent confidence interval - lower 0.085 0.089
90 Percent confidence interval - upper 0.127 0.131
P-value H_0: RMSEA <= 0.050 0.000 0.000
P-value H_0: RMSEA >= 0.080 0.979 0.989
Robust RMSEA 0.108
90 Percent confidence interval - lower 0.088
90 Percent confidence interval - upper 0.128
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.988
Standardized Root Mean Square Residual:
SRMR 0.078 0.078
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [EEF orientation]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.864 0.681
EEC2 1.290 0.195 6.622 0.000 1.114 0.838
EEC3 1.319 0.170 7.751 0.000 1.139 0.906
EEF =~
EEF1 1.000 0.888 0.855
EEF2 1.129 0.160 7.061 0.000 1.003 0.887
EEF3 0.903 0.128 7.079 0.000 0.802 0.809
IM =~
IM1 1.000 0.961 0.881
IM2 0.978 0.142 6.901 0.000 0.940 0.915
IM3 0.693 0.260 2.664 0.008 0.665 0.570
ADT =~
ADT1 1.000 0.823 0.793
ADT2 1.220 0.169 7.233 0.000 1.003 0.914
ADT3 1.280 0.182 7.017 0.000 1.053 0.905
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.549 0.148 3.705 0.000 0.594 0.594
EEC 0.179 0.159 1.124 0.261 0.174 0.174
ADT 0.103 0.142 0.721 0.471 0.095 0.095
EEC ~
IM 0.263 0.146 1.803 0.071 0.292 0.292
ADT 0.211 0.155 1.367 0.172 0.201 0.201
IM ~
ADT 0.613 0.262 2.339 0.019 0.525 0.525
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.143 0.152 27.333 0.000 4.143 3.267
.EEC2 4.271 0.159 26.868 0.000 4.271 3.211
.EEC3 4.400 0.150 29.260 0.000 4.400 3.497
.EEF1 5.471 0.124 44.095 0.000 5.471 5.270
.EEF2 5.543 0.135 41.042 0.000 5.543 4.906
.EEF3 5.600 0.118 47.260 0.000 5.600 5.649
.IM1 5.200 0.130 39.906 0.000 5.200 4.770
.IM2 5.729 0.123 46.665 0.000 5.729 5.578
.IM3 5.443 0.139 39.034 0.000 5.443 4.665
.ADT1 5.457 0.124 44.001 0.000 5.457 5.259
.ADT2 5.371 0.131 40.941 0.000 5.371 4.893
.ADT3 5.300 0.139 38.124 0.000 5.300 4.557
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.862 0.170 5.062 0.000 0.862 0.536
.EEC2 0.527 0.201 2.618 0.009 0.527 0.298
.EEC3 0.285 0.145 1.968 0.049 0.285 0.180
.EEF1 0.289 0.105 2.748 0.006 0.289 0.268
.EEF2 0.271 0.111 2.448 0.014 0.271 0.212
.EEF3 0.339 0.119 2.850 0.004 0.339 0.345
.IM1 0.266 0.112 2.372 0.018 0.266 0.223
.IM2 0.171 0.098 1.757 0.079 0.171 0.163
.IM3 0.918 0.518 1.774 0.076 0.918 0.675
.ADT1 0.400 0.123 3.243 0.001 0.400 0.371
.ADT2 0.198 0.100 1.972 0.049 0.198 0.164
.ADT3 0.245 0.092 2.648 0.008 0.245 0.181
.EEC 0.607 0.165 3.680 0.000 0.812 0.812
.EEF 0.358 0.152 2.348 0.019 0.454 0.454
.IM 0.669 0.167 3.995 0.000 0.724 0.724
ADT 0.677 0.137 4.924 0.000 1.000 1.000
Group 2 [Combination of EEF and EEC]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 1.035 0.759
EEC2 1.135 0.186 6.119 0.000 1.175 0.868
EEC3 1.240 0.156 7.946 0.000 1.283 0.944
EEF =~
EEF1 1.000 1.140 0.940
EEF2 1.067 0.055 19.496 0.000 1.217 0.967
EEF3 0.991 0.099 9.968 0.000 1.130 0.938
IM =~
IM1 1.000 0.988 0.767
IM2 1.162 0.304 3.822 0.000 1.148 0.901
IM3 1.322 0.330 4.010 0.000 1.306 0.948
ADT =~
ADT1 1.000 0.985 0.956
ADT2 0.894 0.069 13.004 0.000 0.881 0.907
ADT3 0.984 0.085 11.591 0.000 0.970 0.831
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.356 0.114 3.111 0.002 0.308 0.308
EEC 0.448 0.173 2.583 0.010 0.406 0.406
ADT 0.282 0.137 2.058 0.040 0.244 0.244
EEC ~
IM 0.389 0.116 3.358 0.001 0.371 0.371
ADT 0.486 0.121 4.009 0.000 0.463 0.463
IM ~
ADT 0.248 0.222 1.118 0.263 0.247 0.247
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.028 0.161 25.057 0.000 4.028 2.953
.EEC2 4.028 0.160 25.247 0.000 4.028 2.975
.EEC3 4.111 0.160 25.656 0.000 4.111 3.024
.EEF1 5.000 0.143 34.966 0.000 5.000 4.121
.EEF2 5.167 0.148 34.841 0.000 5.167 4.106
.EEF3 5.181 0.142 36.461 0.000 5.181 4.297
.IM1 4.917 0.152 32.383 0.000 4.917 3.816
.IM2 5.375 0.150 35.797 0.000 5.375 4.219
.IM3 5.181 0.162 31.908 0.000 5.181 3.760
.ADT1 5.278 0.121 43.462 0.000 5.278 5.122
.ADT2 5.264 0.115 45.965 0.000 5.264 5.417
.ADT3 5.167 0.137 37.578 0.000 5.167 4.429
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.790 0.164 4.805 0.000 0.790 0.425
.EEC2 0.453 0.186 2.434 0.015 0.453 0.247
.EEC3 0.202 0.102 1.991 0.046 0.202 0.109
.EEF1 0.172 0.052 3.299 0.001 0.172 0.117
.EEF2 0.103 0.047 2.180 0.029 0.103 0.065
.EEF3 0.176 0.076 2.302 0.021 0.176 0.121
.IM1 0.684 0.337 2.028 0.043 0.684 0.412
.IM2 0.306 0.084 3.635 0.000 0.306 0.189
.IM3 0.192 0.102 1.874 0.061 0.192 0.101
.ADT1 0.091 0.052 1.746 0.081 0.091 0.085
.ADT2 0.168 0.069 2.426 0.015 0.168 0.178
.ADT3 0.421 0.212 1.985 0.047 0.421 0.309
.EEC 0.602 0.138 4.366 0.000 0.562 0.562
.EEF 0.536 0.119 4.485 0.000 0.412 0.412
.IM 0.916 0.305 3.003 0.003 0.939 0.939
ADT 0.971 0.269 3.608 0.000 1.000 1.000
Group 3 [EEC orientation]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.953 0.765
EEC2 1.459 0.209 6.965 0.000 1.390 0.896
EEC3 1.474 0.182 8.109 0.000 1.405 0.981
EEF =~
EEF1 1.000 1.130 0.911
EEF2 0.983 0.094 10.403 0.000 1.111 0.936
EEF3 0.953 0.069 13.751 0.000 1.077 0.923
IM =~
IM1 1.000 0.832 0.789
IM2 0.823 0.113 7.296 0.000 0.684 0.846
IM3 0.844 0.146 5.781 0.000 0.702 0.906
ADT =~
ADT1 1.000 0.923 0.878
ADT2 1.062 0.116 9.165 0.000 0.980 0.894
ADT3 1.231 0.120 10.222 0.000 1.137 0.910
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.432 0.160 2.708 0.007 0.318 0.318
EEC 0.637 0.216 2.952 0.003 0.537 0.537
ADT 0.046 0.116 0.398 0.690 0.038 0.038
EEC ~
IM 0.549 0.133 4.125 0.000 0.479 0.479
ADT 0.345 0.112 3.077 0.002 0.334 0.334
IM ~
ADT -0.013 0.126 -0.106 0.916 -0.015 -0.015
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 4.569 0.147 31.136 0.000 4.569 3.669
.EEC2 4.583 0.183 25.053 0.000 4.583 2.953
.EEC3 4.569 0.169 27.075 0.000 4.569 3.191
.EEF1 5.375 0.146 36.753 0.000 5.375 4.331
.EEF2 5.583 0.140 39.902 0.000 5.583 4.702
.EEF3 5.667 0.137 41.214 0.000 5.667 4.857
.IM1 5.333 0.124 42.933 0.000 5.333 5.060
.IM2 5.889 0.095 61.774 0.000 5.889 7.280
.IM3 5.806 0.091 63.540 0.000 5.806 7.488
.ADT1 5.431 0.124 43.810 0.000 5.431 5.163
.ADT2 5.361 0.129 41.476 0.000 5.361 4.888
.ADT3 5.278 0.147 35.836 0.000 5.278 4.223
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.642 0.133 4.838 0.000 0.642 0.414
.EEC2 0.476 0.124 3.829 0.000 0.476 0.198
.EEC3 0.076 0.071 1.073 0.283 0.076 0.037
.EEF1 0.263 0.063 4.190 0.000 0.263 0.171
.EEF2 0.176 0.062 2.823 0.005 0.176 0.125
.EEF3 0.201 0.120 1.681 0.093 0.201 0.148
.IM1 0.419 0.214 1.960 0.050 0.419 0.377
.IM2 0.186 0.065 2.862 0.004 0.186 0.285
.IM3 0.108 0.056 1.940 0.052 0.108 0.179
.ADT1 0.253 0.090 2.828 0.005 0.253 0.229
.ADT2 0.242 0.094 2.578 0.010 0.242 0.201
.ADT3 0.269 0.103 2.608 0.009 0.269 0.172
.EEC 0.603 0.182 3.322 0.001 0.664 0.664
.EEF 0.554 0.153 3.612 0.000 0.434 0.434
.IM 0.692 0.193 3.593 0.000 1.000 1.000
ADT 0.853 0.178 4.791 0.000 1.000 1.000GG plot
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
TR as categorical variable
SEM
Grouped by reward
Note: High trust is defined as answering above 5 on the scalelavaan 0.6-19 ended normally after 85 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 66
Number of observations per group:
Control 73
Performance-based reward 69
Model Test User Model:
Standard Scaled
Test Statistic 116.119 132.195
Degrees of freedom 60 60
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.878
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Control 65.749 65.749
Performance-based reward 66.446 66.446
Model Test Baseline Model:
Test statistic 1014.396 750.037
Degrees of freedom 90 90
P-value 0.000 0.000
Scaling correction factor 1.352
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.939 0.891
Tucker-Lewis Index (TLI) 0.909 0.836
Robust Comparative Fit Index (CFI) 0.929
Robust Tucker-Lewis Index (TLI) 0.893
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1534.447 -1534.447
Scaling correction factor 1.778
for the MLR correction
Loglikelihood unrestricted model (H1) -1476.388 -1476.388
Scaling correction factor 1.349
for the MLR correction
Akaike (AIC) 3200.895 3200.895
Bayesian (BIC) 3395.979 3395.979
Sample-size adjusted Bayesian (SABIC) 3187.151 3187.151
Root Mean Square Error of Approximation:
RMSEA 0.115 0.130
90 Percent confidence interval - lower 0.083 0.098
90 Percent confidence interval - upper 0.146 0.162
P-value H_0: RMSEA <= 0.050 0.001 0.000
P-value H_0: RMSEA >= 0.080 0.963 0.994
Robust RMSEA 0.122
90 Percent confidence interval - lower 0.094
90 Percent confidence interval - upper 0.150
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.992
Standardized Root Mean Square Residual:
SRMR 0.069 0.069
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Control]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.955 0.766
EEC2 1.437 0.164 8.744 0.000 1.373 0.878
EEC3 1.372 0.141 9.751 0.000 1.311 0.957
EEF =~
EEF1 1.000 0.994 0.879
EEF2 1.018 0.104 9.828 0.000 1.012 0.884
EEF3 0.996 0.066 15.055 0.000 0.990 0.900
IM =~
IM1 1.000 0.964 0.851
IM2 1.002 0.091 11.034 0.000 0.966 0.985
IM3 0.824 0.154 5.355 0.000 0.794 0.730
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.468 0.125 3.756 0.000 0.454 0.454
EEC 0.429 0.121 3.533 0.000 0.412 0.412
TR_high 0.288 0.201 1.428 0.153 0.289 0.107
EEC ~
IM 0.364 0.129 2.813 0.005 0.367 0.367
TR_high 0.582 0.295 1.972 0.049 0.610 0.226
IM ~
TR_high -0.077 0.268 -0.287 0.774 -0.080 -0.030
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 3.601 0.374 9.637 0.000 3.601 2.887
.EEC2 3.346 0.533 6.274 0.000 3.346 2.140
.EEC3 3.484 0.489 7.122 0.000 3.484 2.543
.EEF1 4.731 0.373 12.671 0.000 4.731 4.184
.EEF2 4.831 0.369 13.092 0.000 4.831 4.220
.EEF3 4.926 0.365 13.502 0.000 4.926 4.480
.IM1 5.336 0.358 14.913 0.000 5.336 4.714
.IM2 5.761 0.356 16.192 0.000 5.761 5.879
.IM3 5.581 0.298 18.700 0.000 5.581 5.134
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.644 0.160 4.029 0.000 0.644 0.414
.EEC2 0.561 0.166 3.384 0.001 0.561 0.229
.EEC3 0.159 0.095 1.671 0.095 0.159 0.085
.EEF1 0.291 0.101 2.881 0.004 0.291 0.227
.EEF2 0.287 0.107 2.673 0.008 0.287 0.219
.EEF3 0.229 0.105 2.192 0.028 0.229 0.190
.IM1 0.353 0.092 3.844 0.000 0.353 0.276
.IM2 0.028 0.050 0.553 0.580 0.028 0.029
.IM3 0.551 0.440 1.253 0.210 0.551 0.466
.EEC 0.747 0.196 3.809 0.000 0.819 0.819
.EEF 0.456 0.153 2.986 0.003 0.461 0.461
.IM 0.928 0.290 3.200 0.001 0.999 0.999
Group 2 [Performance-based reward]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.869 0.672
EEC2 1.293 0.250 5.166 0.000 1.123 0.857
EEC3 1.447 0.244 5.936 0.000 1.257 0.951
EEF =~
EEF1 1.000 1.032 0.898
EEF2 1.046 0.152 6.867 0.000 1.079 0.936
EEF3 0.860 0.118 7.269 0.000 0.888 0.847
IM =~
IM1 1.000 0.926 0.919
IM2 0.613 0.259 2.367 0.018 0.568 0.675
IM3 0.513 0.239 2.142 0.032 0.475 0.533
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.567 0.172 3.291 0.001 0.509 0.509
EEC 0.371 0.210 1.770 0.077 0.313 0.313
TR_high -0.056 0.255 -0.220 0.826 -0.054 -0.019
EEC ~
IM 0.421 0.137 3.080 0.002 0.449 0.449
TR_high 0.512 0.284 1.806 0.071 0.590 0.208
IM ~
TR_high 0.803 0.299 2.681 0.007 0.866 0.305
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 3.504 0.331 10.575 0.000 3.504 2.712
.EEC2 3.335 0.400 8.333 0.000 3.335 2.543
.EEC3 3.200 0.436 7.343 0.000 3.200 2.422
.EEF1 4.732 0.413 11.448 0.000 4.732 4.119
.EEF2 4.869 0.409 11.911 0.000 4.869 4.222
.EEF3 5.079 0.400 12.691 0.000 5.079 4.848
.IM1 4.371 0.391 11.184 0.000 4.371 4.333
.IM2 5.393 0.306 17.614 0.000 5.393 6.409
.IM3 5.282 0.211 25.023 0.000 5.282 5.927
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.915 0.163 5.607 0.000 0.915 0.548
.EEC2 0.458 0.171 2.674 0.007 0.458 0.266
.EEC3 0.166 0.135 1.227 0.220 0.166 0.095
.EEF1 0.255 0.074 3.463 0.001 0.255 0.193
.EEF2 0.165 0.081 2.042 0.041 0.165 0.124
.EEF3 0.310 0.133 2.338 0.019 0.310 0.282
.IM1 0.159 0.169 0.943 0.346 0.159 0.156
.IM2 0.386 0.179 2.155 0.031 0.386 0.545
.IM3 0.569 0.305 1.865 0.062 0.569 0.716
.EEC 0.527 0.190 2.777 0.005 0.698 0.698
.EEF 0.522 0.163 3.196 0.001 0.490 0.490
.IM 0.779 0.297 2.625 0.009 0.907 0.907Grouped by eco condition
lavaan 0.6-19 ended normally after 88 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 66
Number of observations per group:
EEF orientation 70
EEC orientation 72
Model Test User Model:
Standard Scaled
Test Statistic 110.974 106.612
Degrees of freedom 60 60
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.041
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
EEF orientation 47.760 47.760
EEC orientation 58.853 58.853
Model Test Baseline Model:
Test statistic 1046.682 808.080
Degrees of freedom 90 90
P-value 0.000 0.000
Scaling correction factor 1.295
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.947 0.935
Tucker-Lewis Index (TLI) 0.920 0.903
Robust Comparative Fit Index (CFI) 0.948
Robust Tucker-Lewis Index (TLI) 0.922
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1510.004 -1510.004
Scaling correction factor 1.495
for the MLR correction
Loglikelihood unrestricted model (H1) -1454.517 -1454.517
Scaling correction factor 1.279
for the MLR correction
Akaike (AIC) 3152.008 3152.008
Bayesian (BIC) 3347.092 3347.092
Sample-size adjusted Bayesian (SABIC) 3138.264 3138.264
Root Mean Square Error of Approximation:
RMSEA 0.109 0.105
90 Percent confidence interval - lower 0.077 0.072
90 Percent confidence interval - upper 0.141 0.136
P-value H_0: RMSEA <= 0.050 0.003 0.005
P-value H_0: RMSEA >= 0.080 0.934 0.899
Robust RMSEA 0.107
90 Percent confidence interval - lower 0.073
90 Percent confidence interval - upper 0.139
P-value H_0: Robust RMSEA <= 0.050 0.005
P-value H_0: Robust RMSEA >= 0.080 0.907
Standardized Root Mean Square Residual:
SRMR 0.069 0.069
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [EEF orientation]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.868 0.684
EEC2 1.283 0.196 6.555 0.000 1.113 0.837
EEC3 1.313 0.172 7.640 0.000 1.139 0.905
EEF =~
EEF1 1.000 0.878 0.846
EEF2 1.152 0.158 7.300 0.000 1.012 0.895
EEF3 0.911 0.126 7.238 0.000 0.801 0.808
IM =~
IM1 1.000 0.922 0.846
IM2 1.060 0.123 8.643 0.000 0.977 0.951
IM3 0.739 0.233 3.171 0.002 0.682 0.584
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.591 0.149 3.965 0.000 0.621 0.621
EEC 0.207 0.155 1.334 0.182 0.205 0.205
TR_high 0.029 0.226 0.129 0.898 0.033 0.012
EEC ~
IM 0.349 0.135 2.581 0.010 0.371 0.371
TR_high 0.355 0.272 1.305 0.192 0.410 0.154
IM ~
TR_high 0.268 0.298 0.899 0.369 0.290 0.109
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 3.617 0.353 10.240 0.000 3.617 2.852
.EEC2 3.597 0.431 8.352 0.000 3.597 2.704
.EEC3 3.710 0.431 8.612 0.000 3.710 2.949
.EEF1 5.143 0.360 14.294 0.000 5.143 4.954
.EEF2 5.165 0.402 12.853 0.000 5.165 4.571
.EEF3 5.301 0.346 15.300 0.000 5.301 5.347
.IM1 4.886 0.377 12.945 0.000 4.886 4.482
.IM2 5.396 0.383 14.100 0.000 5.396 5.254
.IM3 5.211 0.298 17.466 0.000 5.211 4.467
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.855 0.172 4.966 0.000 0.855 0.532
.EEC2 0.531 0.204 2.599 0.009 0.531 0.300
.EEC3 0.286 0.151 1.893 0.058 0.286 0.181
.EEF1 0.306 0.109 2.806 0.005 0.306 0.284
.EEF2 0.253 0.104 2.435 0.015 0.253 0.198
.EEF3 0.342 0.123 2.774 0.006 0.342 0.348
.IM1 0.339 0.093 3.652 0.000 0.339 0.285
.IM2 0.100 0.075 1.335 0.182 0.100 0.095
.IM3 0.896 0.495 1.811 0.070 0.896 0.659
.EEC 0.622 0.166 3.751 0.000 0.826 0.826
.EEF 0.364 0.154 2.356 0.018 0.472 0.472
.IM 0.840 0.290 2.896 0.004 0.988 0.988
Group 2 [EEC orientation]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.956 0.767
EEC2 1.455 0.208 7.012 0.000 1.391 0.896
EEC3 1.469 0.182 8.054 0.000 1.404 0.980
EEF =~
EEF1 1.000 1.131 0.911
EEF2 0.982 0.094 10.493 0.000 1.110 0.935
EEF3 0.953 0.069 13.766 0.000 1.077 0.923
IM =~
IM1 1.000 0.831 0.788
IM2 0.822 0.114 7.212 0.000 0.683 0.844
IM3 0.847 0.150 5.642 0.000 0.704 0.908
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF ~
IM 0.424 0.159 2.670 0.008 0.311 0.311
EEC 0.635 0.207 3.071 0.002 0.537 0.537
TR_high 0.158 0.188 0.839 0.402 0.140 0.048
EEC ~
IM 0.505 0.126 4.011 0.000 0.439 0.439
TR_high 0.822 0.236 3.482 0.000 0.860 0.297
IM ~
TR_high 0.287 0.293 0.979 0.327 0.345 0.119
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 3.468 0.340 10.214 0.000 3.468 2.785
.EEC2 2.981 0.497 6.000 0.000 2.981 1.920
.EEC3 2.952 0.467 6.323 0.000 2.952 2.061
.EEF1 4.358 0.393 11.097 0.000 4.358 3.511
.EEF2 4.584 0.373 12.281 0.000 4.584 3.861
.EEF3 4.697 0.382 12.290 0.000 4.697 4.026
.IM1 5.007 0.364 13.756 0.000 5.007 4.750
.IM2 5.620 0.295 19.045 0.000 5.620 6.948
.IM3 5.529 0.286 19.337 0.000 5.529 7.131
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.638 0.133 4.776 0.000 0.638 0.411
.EEC2 0.475 0.123 3.861 0.000 0.475 0.197
.EEC3 0.080 0.067 1.199 0.231 0.080 0.039
.EEF1 0.262 0.062 4.230 0.000 0.262 0.170
.EEF2 0.177 0.063 2.794 0.005 0.177 0.126
.EEF3 0.200 0.119 1.681 0.093 0.200 0.147
.IM1 0.421 0.216 1.945 0.052 0.421 0.379
.IM2 0.188 0.067 2.829 0.005 0.188 0.288
.IM3 0.105 0.057 1.849 0.064 0.105 0.175
.EEC 0.628 0.189 3.318 0.001 0.687 0.687
.EEF 0.552 0.153 3.616 0.000 0.432 0.432
.IM 0.680 0.187 3.646 0.000 0.986 0.986GGplot
Reward
Eco-condition
EEF
EEC
IM
Complete theoretical model
Two DVs with dummies for moderators
Complete theoretical modellavaan 0.6-19 ended normally after 111 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 48
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 237.626 252.700
Degrees of freedom 150 150
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.940
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1771.294 1633.267
Degrees of freedom 189 189
P-value 0.000 0.000
Scaling correction factor 1.085
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.945 0.929
Tucker-Lewis Index (TLI) 0.930 0.910
Robust Comparative Fit Index (CFI) 0.938
Robust Tucker-Lewis Index (TLI) 0.922
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -2355.129 -2355.129
Scaling correction factor 1.621
for the MLR correction
Loglikelihood unrestricted model (H1) -2236.316 -2236.316
Scaling correction factor 1.105
for the MLR correction
Akaike (AIC) 4806.259 4806.259
Bayesian (BIC) 4967.826 4967.826
Sample-size adjusted Bayesian (SABIC) 4815.726 4815.726
Root Mean Square Error of Approximation:
RMSEA 0.052 0.057
90 Percent confidence interval - lower 0.039 0.044
90 Percent confidence interval - upper 0.065 0.069
P-value H_0: RMSEA <= 0.050 0.372 0.189
P-value H_0: RMSEA >= 0.080 0.000 0.001
Robust RMSEA 0.055
90 Percent confidence interval - lower 0.043
90 Percent confidence interval - upper 0.066
P-value H_0: Robust RMSEA <= 0.050 0.241
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.056 0.056
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.974 0.741
EEC2 1.291 0.109 11.843 0.000 1.257 0.876
EEC3 1.324 0.093 14.306 0.000 1.289 0.944
EEF =~
EEF1 1.000 1.078 0.909
EEF2 1.046 0.052 20.051 0.000 1.128 0.933
EEF3 0.962 0.054 17.777 0.000 1.037 0.904
IM =~
IM1 1.000 0.955 0.822
IM2 0.992 0.178 5.589 0.000 0.948 0.881
IM3 0.994 0.196 5.066 0.000 0.949 0.816
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
reward1_eco1 0.130 0.406 0.321 0.748 0.136 0.049
reward0_eco2 0.415 0.398 1.042 0.297 0.434 0.162
reward1_eco2 0.600 0.342 1.755 0.079 0.628 0.235
reward0_eco3 -0.179 0.404 -0.444 0.657 -0.188 -0.070
reward1_eco3 -0.037 0.442 -0.084 0.933 -0.039 -0.015
ADT_high_num 0.557 0.352 1.582 0.114 0.583 0.291
TR_high_num -0.062 0.287 -0.217 0.828 -0.065 -0.023
reward1_c1_ADT 0.074 0.448 0.166 0.868 0.078 0.024
reward0_c2_ADT -0.265 0.458 -0.579 0.562 -0.277 -0.084
reward1_c2_ADT -0.500 0.428 -1.169 0.243 -0.523 -0.129
reward0_c3_ADT 0.300 0.494 0.607 0.544 0.314 0.078
reward1_c3_ADT -0.165 0.517 -0.319 0.750 -0.172 -0.048
reward1_ec1_TR 0.530 0.482 1.099 0.272 0.555 0.075
reward0_ec2_TR 0.129 0.554 0.234 0.815 0.135 0.018
reward1_ec2_TR 0.481 0.415 1.158 0.247 0.503 0.083
reward0_ec3_TR 0.895 0.517 1.733 0.083 0.937 0.090
reward1_ec3_TR -0.572 0.606 -0.944 0.345 -0.598 -0.099
EEF ~
IM 0.704 0.077 9.097 0.000 0.624 0.624
reward1_eco1 0.095 0.185 0.513 0.608 0.088 0.032
reward0_eco2 -0.188 0.203 -0.925 0.355 -0.174 -0.065
reward1_eco2 -0.001 0.194 -0.005 0.996 -0.001 -0.000
reward0_eco3 -0.231 0.179 -1.292 0.196 -0.215 -0.080
reward1_eco3 -0.068 0.203 -0.336 0.737 -0.063 -0.024
EEC ~
IM 0.499 0.078 6.403 0.000 0.489 0.489
reward1_eco1 0.152 0.206 0.739 0.460 0.156 0.056
reward0_eco2 0.133 0.221 0.604 0.546 0.137 0.051
reward1_eco2 0.175 0.214 0.821 0.412 0.180 0.067
reward0_eco3 0.051 0.185 0.277 0.782 0.053 0.020
reward1_eco3 0.002 0.222 0.008 0.994 0.002 0.001
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC ~~
.EEF 0.339 0.115 2.940 0.003 0.490 0.490
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.780 0.093 8.412 0.000 0.780 0.451
.EEC2 0.477 0.104 4.591 0.000 0.477 0.232
.EEC3 0.204 0.062 3.303 0.001 0.204 0.109
.EEF1 0.246 0.046 5.370 0.000 0.246 0.174
.EEF2 0.188 0.046 4.120 0.000 0.188 0.129
.EEF3 0.240 0.063 3.803 0.000 0.240 0.183
.IM1 0.439 0.182 2.415 0.016 0.439 0.324
.IM2 0.259 0.084 3.084 0.002 0.259 0.224
.IM3 0.451 0.229 1.970 0.049 0.451 0.333
.EEC 0.701 0.120 5.835 0.000 0.739 0.739
.EEF 0.684 0.171 3.994 0.000 0.589 0.589
.IM 0.761 0.189 4.021 0.000 0.834 0.834
R-Square:
Estimate
EEC1 0.549
EEC2 0.768
EEC3 0.891
EEF1 0.826
EEF2 0.871
EEF3 0.817
IM1 0.676
IM2 0.776
IM3 0.667
EEC 0.261
EEF 0.411
IM 0.166Complete theoretical model with partial mediationlavaan 0.6-19 ended normally after 9 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 33
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 49.175 60.142
Degrees of freedom 24 24
P-value (Chi-square) 0.002 0.000
Scaling correction factor 0.818
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 290.659 324.975
Degrees of freedom 54 54
P-value 0.000 0.000
Scaling correction factor 0.894
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.894 0.867
Tucker-Lewis Index (TLI) 0.761 0.700
Robust Comparative Fit Index (CFI) 0.878
Robust Tucker-Lewis Index (TLI) 0.726
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -861.722 -861.722
Scaling correction factor 1.009
for the MLR correction
Loglikelihood unrestricted model (H1) -837.134 -837.134
Scaling correction factor 0.928
for the MLR correction
Akaike (AIC) 1789.443 1789.443
Bayesian (BIC) 1900.521 1900.521
Sample-size adjusted Bayesian (SABIC) 1795.952 1795.952
Root Mean Square Error of Approximation:
RMSEA 0.070 0.084
90 Percent confidence interval - lower 0.042 0.055
90 Percent confidence interval - upper 0.098 0.114
P-value H_0: RMSEA <= 0.050 0.113 0.029
P-value H_0: RMSEA >= 0.080 0.299 0.615
Robust RMSEA 0.076
90 Percent confidence interval - lower 0.052
90 Percent confidence interval - upper 0.100
P-value H_0: Robust RMSEA <= 0.050 0.038
P-value H_0: Robust RMSEA >= 0.080 0.412
Standardized Root Mean Square Residual:
SRMR 0.040 0.040
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM_composite ~
reward1_eco1 0.105 0.394 0.266 0.790 0.105 0.037
reward0_eco2 0.430 0.396 1.084 0.278 0.430 0.158
reward1_eco2 0.590 0.340 1.733 0.083 0.590 0.218
reward0_eco3 -0.183 0.409 -0.447 0.655 -0.183 -0.068
reward1_eco3 -0.028 0.434 -0.065 0.948 -0.028 -0.010
ADT_high_num 0.522 0.322 1.621 0.105 0.522 0.257
TR_high_num -0.063 0.297 -0.214 0.831 -0.063 -0.022
reward1_c1_ADT 0.104 0.440 0.236 0.813 0.104 0.031
reward0_c2_ADT -0.258 0.456 -0.565 0.572 -0.258 -0.077
reward1_c2_ADT -0.492 0.409 -1.202 0.229 -0.492 -0.120
reward0_c3_ADT 0.308 0.502 0.614 0.539 0.308 0.075
reward1_c3_ADT -0.180 0.507 -0.355 0.723 -0.180 -0.049
reward1_ec1_TR 0.452 0.552 0.820 0.412 0.452 0.060
reward0_ec2_TR 0.073 0.549 0.133 0.895 0.073 0.010
reward1_ec2_TR 0.452 0.419 1.079 0.281 0.452 0.074
reward0_ec3_TR 0.869 0.537 1.619 0.105 0.869 0.082
reward1_ec3_TR -0.575 0.572 -1.005 0.315 -0.575 -0.094
EEF_composite ~
IM_composite 0.625 0.068 9.145 0.000 0.625 0.568
reward1_eco1 0.132 0.186 0.707 0.480 0.132 0.043
reward0_eco2 -0.173 0.205 -0.843 0.399 -0.173 -0.058
reward1_eco2 0.031 0.191 0.161 0.872 0.031 0.010
reward0_eco3 -0.242 0.180 -1.346 0.178 -0.242 -0.081
reward1_eco3 -0.104 0.205 -0.504 0.614 -0.104 -0.035
EEC_composite ~
IM_composite 0.592 0.072 8.179 0.000 0.592 0.486
reward1_eco1 0.206 0.246 0.839 0.401 0.206 0.060
reward0_eco2 0.227 0.262 0.868 0.386 0.227 0.069
reward1_eco2 0.313 0.239 1.313 0.189 0.313 0.095
reward0_eco3 0.098 0.212 0.464 0.643 0.098 0.030
reward1_eco3 0.020 0.261 0.075 0.940 0.020 0.006
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF_composite ~~
.EEC_composite 0.444 0.096 4.625 0.000 0.444 0.466
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.IM_composite 0.886 0.110 8.034 0.000 0.886 0.862
.EEF_composite 0.810 0.124 6.525 0.000 0.810 0.651
.EEC_composite 1.123 0.116 9.704 0.000 1.123 0.736
R-Square:
Estimate
IM_composite 0.138
EEF_composite 0.349
EEC_composite 0.264Complete theoretical model with full mediationlavaan 0.6-19 ended normally after 9 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 23
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 56.516 64.619
Degrees of freedom 34 34
P-value (Chi-square) 0.009 0.001
Scaling correction factor 0.875
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 290.659 324.975
Degrees of freedom 54 54
P-value 0.000 0.000
Scaling correction factor 0.894
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.905 0.887
Tucker-Lewis Index (TLI) 0.849 0.821
Robust Comparative Fit Index (CFI) 0.890
Robust Tucker-Lewis Index (TLI) 0.825
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -865.392 -865.392
Scaling correction factor 1.008
for the MLR correction
Loglikelihood unrestricted model (H1) -837.134 -837.134
Scaling correction factor 0.928
for the MLR correction
Akaike (AIC) 1776.784 1776.784
Bayesian (BIC) 1854.202 1854.202
Sample-size adjusted Bayesian (SABIC) 1781.321 1781.321
Root Mean Square Error of Approximation:
RMSEA 0.056 0.065
90 Percent confidence interval - lower 0.028 0.038
90 Percent confidence interval - upper 0.081 0.090
P-value H_0: RMSEA <= 0.050 0.335 0.161
P-value H_0: RMSEA >= 0.080 0.054 0.176
Robust RMSEA 0.061
90 Percent confidence interval - lower 0.038
90 Percent confidence interval - upper 0.083
P-value H_0: Robust RMSEA <= 0.050 0.204
P-value H_0: Robust RMSEA >= 0.080 0.080
Standardized Root Mean Square Residual:
SRMR 0.042 0.042
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM_composite ~
reward1_eco1 0.105 0.394 0.266 0.790 0.105 0.037
reward0_eco2 0.430 0.396 1.084 0.278 0.430 0.158
reward1_eco2 0.590 0.340 1.733 0.083 0.590 0.218
reward0_eco3 -0.183 0.409 -0.447 0.655 -0.183 -0.068
reward1_eco3 -0.028 0.434 -0.065 0.948 -0.028 -0.010
ADT_high_num 0.522 0.322 1.621 0.105 0.522 0.257
TR_high_num -0.063 0.297 -0.214 0.831 -0.063 -0.022
reward1_c1_ADT 0.104 0.440 0.236 0.813 0.104 0.031
reward0_c2_ADT -0.258 0.456 -0.565 0.572 -0.258 -0.077
reward1_c2_ADT -0.492 0.409 -1.202 0.229 -0.492 -0.120
reward0_c3_ADT 0.308 0.502 0.614 0.539 0.308 0.075
reward1_c3_ADT -0.180 0.507 -0.355 0.723 -0.180 -0.049
reward1_ec1_TR 0.452 0.552 0.820 0.412 0.452 0.060
reward0_ec2_TR 0.073 0.549 0.133 0.895 0.073 0.010
reward1_ec2_TR 0.452 0.419 1.079 0.281 0.452 0.074
reward0_ec3_TR 0.869 0.537 1.619 0.105 0.869 0.082
reward1_ec3_TR -0.575 0.572 -1.005 0.315 -0.575 -0.094
EEF_composite ~
IM_composite 0.638 0.070 9.154 0.000 0.638 0.580
EEC_composite ~
IM_composite 0.616 0.073 8.481 0.000 0.616 0.506
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF_composite ~~
.EEC_composite 0.447 0.096 4.675 0.000 0.447 0.462
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.IM_composite 0.886 0.110 8.034 0.000 0.886 0.862
.EEF_composite 0.825 0.124 6.670 0.000 0.825 0.664
.EEC_composite 1.136 0.115 9.837 0.000 1.136 0.744
R-Square:
Estimate
IM_composite 0.138
EEF_composite 0.336
EEC_composite 0.256Only EEF as DV
Complete theoretical modellavaan 0.6-19 ended normally after 94 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 140.264 159.618
Degrees of freedom 93 93
P-value (Chi-square) 0.001 0.000
Scaling correction factor 0.879
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 1168.896 1068.412
Degrees of freedom 117 117
P-value 0.000 0.000
Scaling correction factor 1.094
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.955 0.930
Tucker-Lewis Index (TLI) 0.943 0.912
Robust Comparative Fit Index (CFI) 0.944
Robust Tucker-Lewis Index (TLI) 0.929
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1494.166 -1494.166
Scaling correction factor 1.923
for the MLR correction
Loglikelihood unrestricted model (H1) -1424.034 -1424.034
Scaling correction factor 1.133
for the MLR correction
Akaike (AIC) 3048.332 3048.332
Bayesian (BIC) 3149.311 3149.311
Sample-size adjusted Bayesian (SABIC) 3054.249 3054.249
Root Mean Square Error of Approximation:
RMSEA 0.049 0.058
90 Percent confidence interval - lower 0.031 0.041
90 Percent confidence interval - upper 0.065 0.074
P-value H_0: RMSEA <= 0.050 0.534 0.205
P-value H_0: RMSEA >= 0.080 0.000 0.010
Robust RMSEA 0.054
90 Percent confidence interval - lower 0.040
90 Percent confidence interval - upper 0.068
P-value H_0: Robust RMSEA <= 0.050 0.300
P-value H_0: Robust RMSEA >= 0.080 0.001
Standardized Root Mean Square Residual:
SRMR 0.043 0.043
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEF =~
EEF1 1.000 1.078 0.909
EEF2 1.046 0.052 20.221 0.000 1.128 0.933
EEF3 0.962 0.055 17.480 0.000 1.037 0.904
IM =~
IM1 1.000 0.946 0.813
IM2 1.012 0.168 6.023 0.000 0.957 0.889
IM3 1.006 0.185 5.445 0.000 0.951 0.818
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
reward1_eco1 0.135 0.404 0.335 0.738 0.143 0.052
reward0_eco2 0.397 0.393 1.009 0.313 0.420 0.157
reward1_eco2 0.600 0.338 1.773 0.076 0.635 0.237
reward0_eco3 -0.207 0.395 -0.524 0.601 -0.219 -0.082
reward1_eco3 -0.027 0.437 -0.062 0.950 -0.029 -0.011
ADT_high_num 0.544 0.344 1.582 0.114 0.575 0.288
TR_high_num -0.082 0.283 -0.288 0.773 -0.086 -0.030
reward1_c1_ADT 0.080 0.444 0.180 0.857 0.085 0.026
reward0_c2_ADT -0.275 0.452 -0.608 0.543 -0.290 -0.088
reward1_c2_ADT -0.506 0.422 -1.200 0.230 -0.536 -0.132
reward0_c3_ADT 0.307 0.486 0.630 0.528 0.324 0.080
reward1_c3_ADT -0.192 0.512 -0.375 0.708 -0.203 -0.056
reward1_ec1_TR 0.541 0.481 1.125 0.261 0.573 0.078
reward0_ec2_TR 0.129 0.545 0.237 0.812 0.137 0.019
reward1_ec2_TR 0.472 0.409 1.154 0.249 0.500 0.082
reward0_ec3_TR 0.883 0.513 1.719 0.086 0.934 0.090
reward1_ec3_TR -0.577 0.603 -0.958 0.338 -0.611 -0.101
EEF ~
IM 0.715 0.077 9.307 0.000 0.627 0.627
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEF1 0.245 0.046 5.291 0.000 0.245 0.174
.EEF2 0.188 0.047 4.037 0.000 0.188 0.129
.EEF3 0.240 0.065 3.679 0.000 0.240 0.183
.IM1 0.457 0.171 2.679 0.007 0.457 0.338
.IM2 0.242 0.070 3.454 0.001 0.242 0.209
.IM3 0.448 0.218 2.049 0.040 0.448 0.331
.EEF 0.706 0.168 4.199 0.000 0.607 0.607
.IM 0.745 0.181 4.123 0.000 0.833 0.833Only EEC as DV
Complete theoretical modellavaan 0.6-19 ended normally after 92 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Number of observations 214
Model Test User Model:
Standard Scaled
Test Statistic 159.838 165.561
Degrees of freedom 93 93
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.965
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 980.770 868.817
Degrees of freedom 117 117
P-value 0.000 0.000
Scaling correction factor 1.129
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.923 0.903
Tucker-Lewis Index (TLI) 0.903 0.879
Robust Comparative Fit Index (CFI) 0.917
Robust Tucker-Lewis Index (TLI) 0.896
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1694.172 -1694.172
Scaling correction factor 1.702
for the MLR correction
Loglikelihood unrestricted model (H1) -1614.253 -1614.253
Scaling correction factor 1.145
for the MLR correction
Akaike (AIC) 3448.345 3448.345
Bayesian (BIC) 3549.324 3549.324
Sample-size adjusted Bayesian (SABIC) 3454.261 3454.261
Root Mean Square Error of Approximation:
RMSEA 0.058 0.060
90 Percent confidence interval - lower 0.042 0.045
90 Percent confidence interval - upper 0.073 0.075
P-value H_0: RMSEA <= 0.050 0.189 0.129
P-value H_0: RMSEA >= 0.080 0.007 0.015
Robust RMSEA 0.059
90 Percent confidence interval - lower 0.044
90 Percent confidence interval - upper 0.074
P-value H_0: Robust RMSEA <= 0.050 0.145
P-value H_0: Robust RMSEA >= 0.080 0.009
Standardized Root Mean Square Residual:
SRMR 0.054 0.054
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EEC =~
EEC1 1.000 0.967 0.736
EEC2 1.292 0.109 11.800 0.000 1.249 0.871
EEC3 1.345 0.096 14.069 0.000 1.301 0.952
IM =~
IM1 1.000 0.922 0.793
IM2 1.049 0.162 6.484 0.000 0.968 0.899
IM3 1.046 0.172 6.095 0.000 0.965 0.830
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
IM ~
reward1_eco1 0.137 0.387 0.355 0.723 0.149 0.054
reward0_eco2 0.400 0.384 1.044 0.297 0.434 0.162
reward1_eco2 0.582 0.334 1.742 0.081 0.631 0.236
reward0_eco3 -0.184 0.389 -0.473 0.636 -0.199 -0.074
reward1_eco3 -0.033 0.427 -0.077 0.938 -0.036 -0.013
ADT_high_num 0.483 0.326 1.483 0.138 0.524 0.262
TR_high_num -0.064 0.275 -0.233 0.816 -0.069 -0.024
reward1_c1_ADT 0.098 0.429 0.229 0.819 0.107 0.032
reward0_c2_ADT -0.224 0.437 -0.514 0.607 -0.243 -0.074
reward1_c2_ADT -0.445 0.405 -1.098 0.272 -0.482 -0.119
reward0_c3_ADT 0.322 0.473 0.680 0.497 0.349 0.086
reward1_c3_ADT -0.127 0.503 -0.253 0.800 -0.138 -0.038
reward1_ec1_TR 0.501 0.445 1.126 0.260 0.543 0.074
reward0_ec2_TR 0.063 0.525 0.121 0.904 0.069 0.009
reward1_ec2_TR 0.446 0.388 1.149 0.250 0.484 0.080
reward0_ec3_TR 0.802 0.501 1.599 0.110 0.869 0.084
reward1_ec3_TR -0.654 0.594 -1.102 0.271 -0.709 -0.117
EEC ~
IM 0.507 0.082 6.207 0.000 0.483 0.483
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.EEC1 0.793 0.092 8.618 0.000 0.793 0.459
.EEC2 0.497 0.114 4.362 0.000 0.497 0.241
.EEC3 0.174 0.069 2.527 0.011 0.174 0.093
.IM1 0.501 0.164 3.053 0.002 0.501 0.370
.IM2 0.221 0.055 4.057 0.000 0.221 0.191
.IM3 0.421 0.194 2.166 0.030 0.421 0.311
.EEC 0.717 0.115 6.220 0.000 0.767 0.767
.IM 0.713 0.175 4.065 0.000 0.838 0.838With moderators as latent variables
Nested modelling
Mediation - doesnt work for Quarto
No mediation vs. mediationFull vs. partial mediationEntire model with full vs. partial mediationModeration
mediation vs. moderation aic bic srmr rmsea cfi tli
1772.686 1809.712 0.024 0.000 1.000 1.024 aic bic srmr rmsea cfi tli
1776.784 1854.202 0.042 0.056 0.905 0.849